Computational economics explores the intersection of economics and computation. Areas encompassed under computational economics include agent-based computational modeling, computational econometrics and statistics, computational finance, computational modeling of dynamic macroeconomic systems, of transaction costs, computational tools for the design of automated Internet markets, programming tools specifically designed for computational economics, and pedagogical tools for the teaching of computational economics. Some of these areas are unique to computational economics, while others extend traditional areas of economics by solving problems that are difficult to study without the use of computers.
Computational economics researchers use computational tools both for computational economic modeling and for the computational solution of analytically and statistically formulated economic problems.
For example, with regard to computational modeling tools, Agent-Based Computational Economics (ACE) is the computational study of economic processes modeled as dynamic systems of interacting agents. Here "agent" refers broadly to a bundle of data and behavioral methods representing an entity constituting part of a computationally constructed world. Agents can represent social, biological, and/or physical entities. Starting from initial conditions determined by the modeler, an ACE model develops forward through time driven solely by agent interactions.
With regard to computational solution tools, examples include software for carrying out various matrix operations (e.g. matrix inversion) and for solving systems of linear and nonlinear equations. For a repository of public-domain computational solution tools, visit here.
The following journals specialize in computational economics: Computational Economics, Journal of Applied Econometrics, and the Journal of Economic Dynamics and Control.
[edit] References
Handbook of Computational Economics:
Hans M. Amman, David A. Kendrick, John Rust (1996), v. 1. Description & contents.
Leigh Tesfatsion and Kenneth Judd, ed. (2006), v. 2. Description & contents.
Vassilis A. Hajivassiliou (2008). "computational methods in econometrics," The New Palgrave Dictionary of Economics. 2nd Edition. Abstract.
Felix Kubler (2008). "computation of general equilibria (new developments)," The New Palgrave Dictionary of Economics. 2nd Edition Abstract.
Herbert E. Scarf (2008). "computation of general equilibria," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
Monday, June 1, 2009
Model (Macro Economics)
A model in macroeconomics is a logical, mathematical, and/or computational framework designed to describe the operation of a national or regional economy, and especially the dynamics of aggregate quantities such as the total amount of goods and services produced, total income earned, the level of employment of productive resources, and the level of prices.
There are different types of macroeconomic models that serve different purposes and have different advantages and disadvantages. Models are used to clarify and illustrate basic theoretical principles in macroeconomics; they are used to test, compare, and quantify different theories of the macroeconomy; they are used to produce “what if” scenarios, and especially to evaluate the possible effects of changes in monetary, fiscal, or other macroeconomic policies; and they are used for generating economic forecasts. Thus macroeconomic models are used in academic teaching and research, and are also widely used by international organizations, national governments and larger corporations, as well as by economics consultancies and think tanks.
Contents
1 Types of macroeconomic models
1.1 Simple theoretical models
1.2 Empirical forecasting models
1.2.1 The Lucas critique of empirical forecasting models
1.3 Dynamic stochastic general equilibrium models
1.3.1 DSGE versus CGE models
1.4 Agent-based computational macroeconomic models
1.4.1 Strengths and weaknesses of DSGE and ACE models
2 References
3 See also
4 External links
[edit] Types of macroeconomic models
[edit] Simple theoretical models
Simple textbook descriptions of the macroeconomy involving a small number of equations or diagrams are often called ‘models’. Examples include the IS-LM model and Mundell-Fleming model of Keynesian macroeconomics, and the Solow model of neoclassical growth theory. These models share several features. They are based on a few equations involving a few variables, which can often be explained with simple diagrams.[1] Many of these models are static, but some are dynamic, describing the economy over many time periods. The variables that appear in these models often represent macroeconomic aggregates (such as GDP or total employment) rather than individual choice variables, and while the equations relating these variables are intended to describe economic decisions, they are not usually derived directly by aggregating models of individual choices. They are simple enough to be used as illustrations of theoretical points in introductory explanations of macroeconomic ideas; but therefore quantitative application to forecasting, testing, or policy evaluation is usually impossible without substantially augmenting the structure of the model.
[edit] Empirical forecasting models
In the 1940s and 1950s, as governments began accumulating national income and product accounting data, economists set out to construct quantitative models to describe the dynamics observed in the data. These models estimated the relations between different macroeconomic variables using (mostly linear) time series analysis. Like the simpler theoretical models, these empirical models described relations between aggregate quantities, but many addressed a much finer level of detail (for example, studying the relations between output, employment, investment, and other variables in many different industries). Thus, these models grew to include hundreds or thousands of equations describing the evolution of hundreds or thousands of prices and quantities over time, making computers essential for their solution. While the choice of which variables to include in each equation was partly guided by economic theory (for example, including past income as a determinant of consumption, as suggested by the theory of adaptive expectations), variable inclusion was mostly determined on purely empirical grounds.
Dutch economist Jan Tinbergen developed the first comprehensive national model, which he first built for the Netherlands and later applied to the United States and the United Kingdom after World War II. The first global macroeconomic model, Wharton Econometric Forecasting Associates' LINK project, was initiated by Lawrence Klein. The model was cited in 1980 when Klein, like Tinbergen before him, won the Nobel Prize. Large-scale empirical models of this type, including the Wharton model, are still in use today, especially for forecasting purposes.[2][3]
[edit] The Lucas critique of empirical forecasting models
Main article: Lucas critique
Econometric studies in the first part of the 20th century showed a negative correlation between inflation and unemployment called the Phillips curve.[4] Empirical macroeconomic forecasting models, being based on roughly the same data, had similar implications: they suggested that unemployment could be permanently lowered by permanently increasing inflation. However, in 1968, Milton Friedman[5] and Edmund Phelps[6] argued that this apparent tradeoff was illusory. They claimed that the historical relation between inflation and unemployment was due to the fact that past inflationary episodes had been largely unexpected. They argued that if monetary authorities permanently raised the inflation rate, workers and firms would eventually come to understand this, at which point the economy would return to its previous, higher level of unemployment, but now with higher inflation too. The stagflation of the 1970s appeared to bear out their prediction.[7]
In 1976, Robert Lucas, Jr., published an influential paper[8] arguing that the failure of the Phillips curve in the 1970s was just one example of a general problem with empirical forecasting models. He pointed out that such models are derived from observed relationships between various macroeconomic quantities over time, and that these relations differ depending on what macroeconomic policy regime is in place. In the context of the Phillips curve, this means that the relation between inflation and unemployment observed in an economy where inflation has usually been low in the past would differ from the relation observed in an economy where inflation has been high.[9] Furthermore, this means one cannot predict the effects of a new policy regime using an empirical forecasting model based on data from previous periods when that policy regime was not in place. Lucas argued that economists would remain unable to predict the effects of new policies unless they built models based on economic fundamentals (like preferences, technology, and budget constraints) that should be unaffected by policy changes.
[edit] Dynamic stochastic general equilibrium models
Main article: Dynamic stochastic general equilibrium
Partly as a response to the Lucas critique, economists of the 1980s and 1990s began to construct microfounded[10] macroeconomic models based on rational choice, which have come to be called dynamic stochastic general equilibrium (DSGE) models. These models begin by specifying the set of agents active in the economy, such as households, firms, and governments in one or more countries, as well as the preferences, technology, and budget constraint of each one. Each agent is assumed to make an optimal choice, taking into account prices and the strategies of other agents, both in the current period and in the future. Summing up the decisions of the different types of agents, it is possible to find the prices that equate supply with demand in every market. Thus these models embody a type of equilibrium self-consistency: agents choose optimally given the prices, while prices must be consistent with agents’ supplies and demands.
DSGE models often assume that all agents of a given type are identical (i.e. there is a ‘representative household’ and a ‘representative firm’) and can perform perfect calculations that forecast the future correctly on average (which is called rational expectations). However, these are only simplifying assumptions, and are not essential for the DSGE methodology; many DSGE studies aim for greater realism by considering heterogeneous agents[11] or various types of adaptive expectations[12]. Compared with empirical forecasting models, DSGE models typically have less variables and equations, mainly because DSGE models are harder to solve, even with the help of computers[13]. Simple theoretical DSGE models involving only a few variables have been estimated to attempt to test what mechanisms are most important for explaining business cycles; this empirical work has given rise to two main competing frameworks called the real business cycle model[14][15][16] and the New Keynesian DSGE model.[17][18] More elaborate DSGE models are used to predict the effects of changes in economic policy and evaluate their impact on social welfare. However, economic forecasting is still largely based on more traditional empirical models, which are still widely believed to achieve greater accuracy in predicting the impact of economic disturbances over time.
[edit] DSGE versus CGE models
Main article: Computable general equilibrium
A closely related methodology that pre-dates DSGE modeling is computable general equilibrium (CGE) modeling. Like DSGE models, CGE models are often microfounded on assumptions about preferences, technology, and budget constraints. However, CGE models focus mostly on long-run relationships, making them most suited to studying the long-run impact of permanent policies like the tax system or the openness of the economy to international trade.[19][20] DSGE models instead emphasize the dynamics of the economy over time (often at a quarterly frequency), making them suited for studying business cycles and the cyclical effects of monetary and fiscal policy.
[edit] Agent-based computational macroeconomic models
Main article: Agent-Based Computational Economics
Another modeling methodology which has developed at the same time as DSGE models is that of Agent-based computational economics (ACE).[21] Like the DSGE methodology, ACE seeks to break down aggregate macroeconomic relationships into microeconomic decisions of individual agents. ACE models also begin by defining the set of agents that make up the economy, and specify the types of interactions individual agents can have with each other or with the market as a whole. Instead of defining the preferences of those agents, ACE models often jump directly to specifying their strategies. Or sometimes, preferences are specified, together with an initial strategy and a learning rule whereby the strategy is adjusted according to its past success. Given these strategies, the interaction of large numbers of individual agents (who may be very heterogeneous) can be simulated on a computer, and then the aggregate, macroeconomic relationships that arise from those individual actions can be studied.
[edit] Strengths and weaknesses of DSGE and ACE models
DSGE and ACE models have different advantages and disadvantages due to their different underlying structures. DSGE models may exaggerate individual rationality and foresight, and understate the importance of heterogeneity, since the rational expectations, representative agent case remains the simplest and thus the most common type of DSGE model to solve. Also, unlike ACE models, it is typically very difficult to study local interactions between individual agents in DSGE models, which instead focus mostly on the way agents interact through aggregate prices. On the other hand, ACE models may exaggerate errors in individual decision-making, since the strategies assumed in ACE models may be very far from optimal choices unless the modeler is very careful. A related issue is that ACE models which start from strategies instead of preferences may remain vulnerable to the Lucas critique: a changed policy regime should generally give rise to changed strategies.
[edit] References
^ Blanchard, Olivier (2000), Macroeconomics, 2nd ed., Chap. 3.3, p. 47. Prentice Hall, ISBN 013013306X.
^ Lawrence R. Klein, ed. (1991), Comparative Performance of US Econometric Models. Oxford University Press, ISBN 0195057724.
^ Eckstein, Otto (1983), The DRI Model of the US Economy. McGraw-Hill, DOI-10.2307/1058399, ISBN 0070189722.
^ A.W. Phillips (1958), The relationship between unemployment and the rate of change of money wages in the United Kingdom 1861-1957, Economica 25 (100), pp. 283-99.
^ Milton Friedman, 'The role of monetary policy'. American Economic Review 58, pp. 1-17.
^ Edmund S. Phelps (1968), 'Money wage dynamics and labor market equilibrium'. Journal of Political Economy 76 (4), pp. 678-711.
^ Blanchard, Olivier (2000), op. cit., Ch. 28, p. 540.
^ Robert E. Lucas, Jr. (1976). Econometric Policy Evaluation: A Critique. Carnegie-Rochester Conference Series on Public Policy 1: 19–46.
^ Blanchard, Olivier (2000), op. cit., Ch. 28, p. 542.
^ Edmund S. Phelps, ed., (1970), Microeconomic Foundations of Employment and Inflation Theory. New York, Norton and Co. ISBN 0-393-09326-3.
^ Per Krusell and Anthony A. Smith, Jr. (1998), 'Income and wealth heterogeneity in the macroeconomy.' Journal of Political Economy 106 (5), pp. 243-77.
^ George W. Evans and Seppo Honkapohja (2001), Learning and Expectations in Macroeconomics. Princeton University Press, ISBN 0-691-04921-1.
^ DeJong, D. N. with C. Dave (2007), Structural Macroeconometrics. Princeton University Press, ISBN 0691126488.
^ Finn E. Kydland and Edward C. Prescott (1982), 'Time to Build and Aggregate Fluctuations'. Econometrica, 50, 1345-70.
^ Thomas F. Cooley (1995), Frontiers of Business Cycle Research. Princeton University Press.
^ Andrew Abel and Ben Bernanke (1995), Macroeconomics, 2nd ed., Ch. 11.1, pp. 355-362. Addison-Wesley, ISBN 0201543923.
^ Julio Rotemberg and Michael Woodford (1997), 'An optimization-based econometric framework for the evaluation of monetary policy'. NBER Macroeconomics Annual 12, pp. 297-346.
^ Woodford, Michael (2003), Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton University Press, ISBN 0691010498.
^ Shoven, John B., and John Whalley (1972), 'A general equilibrium calculation of the effects of differential taxation of income from capital in the US.' Journal of Public Economics 1, pp. 281-321.
^ Kehoe, Patrick J., and Timothy J. Kehoe (1994), 'A primer on static applied general equilibrium models'. Federal Reserve Bank of Minneapolis Quarterly Review 18 (1).
^ Leigh Tesfatsion (2003), 'Agent-Based Computational Economics', Iowa State University Economics Working Paper #1.
There are different types of macroeconomic models that serve different purposes and have different advantages and disadvantages. Models are used to clarify and illustrate basic theoretical principles in macroeconomics; they are used to test, compare, and quantify different theories of the macroeconomy; they are used to produce “what if” scenarios, and especially to evaluate the possible effects of changes in monetary, fiscal, or other macroeconomic policies; and they are used for generating economic forecasts. Thus macroeconomic models are used in academic teaching and research, and are also widely used by international organizations, national governments and larger corporations, as well as by economics consultancies and think tanks.
Contents
1 Types of macroeconomic models
1.1 Simple theoretical models
1.2 Empirical forecasting models
1.2.1 The Lucas critique of empirical forecasting models
1.3 Dynamic stochastic general equilibrium models
1.3.1 DSGE versus CGE models
1.4 Agent-based computational macroeconomic models
1.4.1 Strengths and weaknesses of DSGE and ACE models
2 References
3 See also
4 External links
[edit] Types of macroeconomic models
[edit] Simple theoretical models
Simple textbook descriptions of the macroeconomy involving a small number of equations or diagrams are often called ‘models’. Examples include the IS-LM model and Mundell-Fleming model of Keynesian macroeconomics, and the Solow model of neoclassical growth theory. These models share several features. They are based on a few equations involving a few variables, which can often be explained with simple diagrams.[1] Many of these models are static, but some are dynamic, describing the economy over many time periods. The variables that appear in these models often represent macroeconomic aggregates (such as GDP or total employment) rather than individual choice variables, and while the equations relating these variables are intended to describe economic decisions, they are not usually derived directly by aggregating models of individual choices. They are simple enough to be used as illustrations of theoretical points in introductory explanations of macroeconomic ideas; but therefore quantitative application to forecasting, testing, or policy evaluation is usually impossible without substantially augmenting the structure of the model.
[edit] Empirical forecasting models
In the 1940s and 1950s, as governments began accumulating national income and product accounting data, economists set out to construct quantitative models to describe the dynamics observed in the data. These models estimated the relations between different macroeconomic variables using (mostly linear) time series analysis. Like the simpler theoretical models, these empirical models described relations between aggregate quantities, but many addressed a much finer level of detail (for example, studying the relations between output, employment, investment, and other variables in many different industries). Thus, these models grew to include hundreds or thousands of equations describing the evolution of hundreds or thousands of prices and quantities over time, making computers essential for their solution. While the choice of which variables to include in each equation was partly guided by economic theory (for example, including past income as a determinant of consumption, as suggested by the theory of adaptive expectations), variable inclusion was mostly determined on purely empirical grounds.
Dutch economist Jan Tinbergen developed the first comprehensive national model, which he first built for the Netherlands and later applied to the United States and the United Kingdom after World War II. The first global macroeconomic model, Wharton Econometric Forecasting Associates' LINK project, was initiated by Lawrence Klein. The model was cited in 1980 when Klein, like Tinbergen before him, won the Nobel Prize. Large-scale empirical models of this type, including the Wharton model, are still in use today, especially for forecasting purposes.[2][3]
[edit] The Lucas critique of empirical forecasting models
Main article: Lucas critique
Econometric studies in the first part of the 20th century showed a negative correlation between inflation and unemployment called the Phillips curve.[4] Empirical macroeconomic forecasting models, being based on roughly the same data, had similar implications: they suggested that unemployment could be permanently lowered by permanently increasing inflation. However, in 1968, Milton Friedman[5] and Edmund Phelps[6] argued that this apparent tradeoff was illusory. They claimed that the historical relation between inflation and unemployment was due to the fact that past inflationary episodes had been largely unexpected. They argued that if monetary authorities permanently raised the inflation rate, workers and firms would eventually come to understand this, at which point the economy would return to its previous, higher level of unemployment, but now with higher inflation too. The stagflation of the 1970s appeared to bear out their prediction.[7]
In 1976, Robert Lucas, Jr., published an influential paper[8] arguing that the failure of the Phillips curve in the 1970s was just one example of a general problem with empirical forecasting models. He pointed out that such models are derived from observed relationships between various macroeconomic quantities over time, and that these relations differ depending on what macroeconomic policy regime is in place. In the context of the Phillips curve, this means that the relation between inflation and unemployment observed in an economy where inflation has usually been low in the past would differ from the relation observed in an economy where inflation has been high.[9] Furthermore, this means one cannot predict the effects of a new policy regime using an empirical forecasting model based on data from previous periods when that policy regime was not in place. Lucas argued that economists would remain unable to predict the effects of new policies unless they built models based on economic fundamentals (like preferences, technology, and budget constraints) that should be unaffected by policy changes.
[edit] Dynamic stochastic general equilibrium models
Main article: Dynamic stochastic general equilibrium
Partly as a response to the Lucas critique, economists of the 1980s and 1990s began to construct microfounded[10] macroeconomic models based on rational choice, which have come to be called dynamic stochastic general equilibrium (DSGE) models. These models begin by specifying the set of agents active in the economy, such as households, firms, and governments in one or more countries, as well as the preferences, technology, and budget constraint of each one. Each agent is assumed to make an optimal choice, taking into account prices and the strategies of other agents, both in the current period and in the future. Summing up the decisions of the different types of agents, it is possible to find the prices that equate supply with demand in every market. Thus these models embody a type of equilibrium self-consistency: agents choose optimally given the prices, while prices must be consistent with agents’ supplies and demands.
DSGE models often assume that all agents of a given type are identical (i.e. there is a ‘representative household’ and a ‘representative firm’) and can perform perfect calculations that forecast the future correctly on average (which is called rational expectations). However, these are only simplifying assumptions, and are not essential for the DSGE methodology; many DSGE studies aim for greater realism by considering heterogeneous agents[11] or various types of adaptive expectations[12]. Compared with empirical forecasting models, DSGE models typically have less variables and equations, mainly because DSGE models are harder to solve, even with the help of computers[13]. Simple theoretical DSGE models involving only a few variables have been estimated to attempt to test what mechanisms are most important for explaining business cycles; this empirical work has given rise to two main competing frameworks called the real business cycle model[14][15][16] and the New Keynesian DSGE model.[17][18] More elaborate DSGE models are used to predict the effects of changes in economic policy and evaluate their impact on social welfare. However, economic forecasting is still largely based on more traditional empirical models, which are still widely believed to achieve greater accuracy in predicting the impact of economic disturbances over time.
[edit] DSGE versus CGE models
Main article: Computable general equilibrium
A closely related methodology that pre-dates DSGE modeling is computable general equilibrium (CGE) modeling. Like DSGE models, CGE models are often microfounded on assumptions about preferences, technology, and budget constraints. However, CGE models focus mostly on long-run relationships, making them most suited to studying the long-run impact of permanent policies like the tax system or the openness of the economy to international trade.[19][20] DSGE models instead emphasize the dynamics of the economy over time (often at a quarterly frequency), making them suited for studying business cycles and the cyclical effects of monetary and fiscal policy.
[edit] Agent-based computational macroeconomic models
Main article: Agent-Based Computational Economics
Another modeling methodology which has developed at the same time as DSGE models is that of Agent-based computational economics (ACE).[21] Like the DSGE methodology, ACE seeks to break down aggregate macroeconomic relationships into microeconomic decisions of individual agents. ACE models also begin by defining the set of agents that make up the economy, and specify the types of interactions individual agents can have with each other or with the market as a whole. Instead of defining the preferences of those agents, ACE models often jump directly to specifying their strategies. Or sometimes, preferences are specified, together with an initial strategy and a learning rule whereby the strategy is adjusted according to its past success. Given these strategies, the interaction of large numbers of individual agents (who may be very heterogeneous) can be simulated on a computer, and then the aggregate, macroeconomic relationships that arise from those individual actions can be studied.
[edit] Strengths and weaknesses of DSGE and ACE models
DSGE and ACE models have different advantages and disadvantages due to their different underlying structures. DSGE models may exaggerate individual rationality and foresight, and understate the importance of heterogeneity, since the rational expectations, representative agent case remains the simplest and thus the most common type of DSGE model to solve. Also, unlike ACE models, it is typically very difficult to study local interactions between individual agents in DSGE models, which instead focus mostly on the way agents interact through aggregate prices. On the other hand, ACE models may exaggerate errors in individual decision-making, since the strategies assumed in ACE models may be very far from optimal choices unless the modeler is very careful. A related issue is that ACE models which start from strategies instead of preferences may remain vulnerable to the Lucas critique: a changed policy regime should generally give rise to changed strategies.
[edit] References
^ Blanchard, Olivier (2000), Macroeconomics, 2nd ed., Chap. 3.3, p. 47. Prentice Hall, ISBN 013013306X.
^ Lawrence R. Klein, ed. (1991), Comparative Performance of US Econometric Models. Oxford University Press, ISBN 0195057724.
^ Eckstein, Otto (1983), The DRI Model of the US Economy. McGraw-Hill, DOI-10.2307/1058399, ISBN 0070189722.
^ A.W. Phillips (1958), The relationship between unemployment and the rate of change of money wages in the United Kingdom 1861-1957, Economica 25 (100), pp. 283-99.
^ Milton Friedman, 'The role of monetary policy'. American Economic Review 58, pp. 1-17.
^ Edmund S. Phelps (1968), 'Money wage dynamics and labor market equilibrium'. Journal of Political Economy 76 (4), pp. 678-711.
^ Blanchard, Olivier (2000), op. cit., Ch. 28, p. 540.
^ Robert E. Lucas, Jr. (1976). Econometric Policy Evaluation: A Critique. Carnegie-Rochester Conference Series on Public Policy 1: 19–46.
^ Blanchard, Olivier (2000), op. cit., Ch. 28, p. 542.
^ Edmund S. Phelps, ed., (1970), Microeconomic Foundations of Employment and Inflation Theory. New York, Norton and Co. ISBN 0-393-09326-3.
^ Per Krusell and Anthony A. Smith, Jr. (1998), 'Income and wealth heterogeneity in the macroeconomy.' Journal of Political Economy 106 (5), pp. 243-77.
^ George W. Evans and Seppo Honkapohja (2001), Learning and Expectations in Macroeconomics. Princeton University Press, ISBN 0-691-04921-1.
^ DeJong, D. N. with C. Dave (2007), Structural Macroeconometrics. Princeton University Press, ISBN 0691126488.
^ Finn E. Kydland and Edward C. Prescott (1982), 'Time to Build and Aggregate Fluctuations'. Econometrica, 50, 1345-70.
^ Thomas F. Cooley (1995), Frontiers of Business Cycle Research. Princeton University Press.
^ Andrew Abel and Ben Bernanke (1995), Macroeconomics, 2nd ed., Ch. 11.1, pp. 355-362. Addison-Wesley, ISBN 0201543923.
^ Julio Rotemberg and Michael Woodford (1997), 'An optimization-based econometric framework for the evaluation of monetary policy'. NBER Macroeconomics Annual 12, pp. 297-346.
^ Woodford, Michael (2003), Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton University Press, ISBN 0691010498.
^ Shoven, John B., and John Whalley (1972), 'A general equilibrium calculation of the effects of differential taxation of income from capital in the US.' Journal of Public Economics 1, pp. 281-321.
^ Kehoe, Patrick J., and Timothy J. Kehoe (1994), 'A primer on static applied general equilibrium models'. Federal Reserve Bank of Minneapolis Quarterly Review 18 (1).
^ Leigh Tesfatsion (2003), 'Agent-Based Computational Economics', Iowa State University Economics Working Paper #1.
Economics Model
In economics, a model is a theoretical construct that represents economic processes by a set of variables and a set of logical and/or quantitative relationships between them. The economic model is a simplified framework designed to illustrate complex processes, often but not always using mathematical techniques. Frequently, economic models use structural parameters. Structural parameters are underlying parameters in a model or class of models.[1] A model may have various parameters and those parameters may change to create various properties.[2]
Contents
1 Overview
2 Types of models
3 Pitfalls
3.1 Restrictive, unrealistic assumptions
3.2 Omitted details
3.3 Are economic models falsifiable?
4 History
5 Tests of macroeconomic predictions
5.1 Comparison with models in other sciences
5.2 The effects of deterministic chaos on economic models
5.3 The critique of hubris in planning
6 Examples of economic models
7 See also
8 Notes
9 References
10 External links
[edit] Overview
In general terms, economic models have two functions: first as a simplification of and abstraction from observed data, and second as a means of selection of data based on a paradigm of econometric study.
Simplification is particularly important for economics given the enormous complexity of economic processes. This complexity can be attributed to the diversity of factors that determine economic activity; these factors include: individual and cooperative decision processes, resource limitations, environmental and geographical constraints, institutional and legal requirements and purely random fluctuations. Economists therefore must make a reasoned choice of which variables and which relationships between these variables are relevant and which ways of analyzing and presenting this information are useful.
Selection is important because the nature of an economic model will often determine what facts will be looked at, and how they will be compiled. For example inflation is a general economic concept, but to measure inflation requires a model of behavior, so that an economist can differentiate between real changes in price, and changes in price which are to be attributed to inflation.
In addition to their professional academic interest, the use of models include:
Forecasting economic activity in a way in which conclusions are logically related to assumptions;
Proposing economic policy to modify future economic activity;
Presenting reasoned arguments to politically justify economic policy at the national level, to explain and influence company strategy at the level of the firm, or to provide intelligent advice for household economic decisions at the level of households.
Planning and allocation, in the case of centrally planned economies, and on a smaller scale in logistics and management of businesses.
In finance predictive models have been used since the 1980s for trading (investment, and speculation), for example emerging market bonds were often traded based on economic models predicting the growth of the developing nation issuing them. Since the 1990s many long-term risk management models have incorporated economic relationships between simulated variables in an attempt to detect high-exposure future scenarios (often through a Monte Carlo method).
Obviously any kind of reasoning about anything uses representations by variables and logical relationships. A model however establishes an argumentative framework for applying logic and mathematics that can be independently discussed and tested and that can be applied in various instances. Policies and arguments that rely on economic models have a clear basis for soundness, namely the validity of the supporting model.
Economic models in current use do not pretend to be theories of everything economic; any such pretensions would immediately be thwarted by computational infeasibility and the paucity of theories for most types of economic behavior. Therefore conclusions drawn from models will be approximate representations of economic facts. However, properly constructed models can remove extraneous information and isolate useful approximations of key relationships. In this way more can be understood about the relationships in question than by trying to understand the entire economic process.
The details of model construction vary with type of model and its application, but a generic process can be identified. Generally any modelling process has two steps: generating a model, then checking the model for accuracy (sometimes called diagnostics). The diagnostic step is important because a model is only useful to the extent that it accurately mirrors the relationships that it purports to describe. Creating and diagnosing a model is frequently an iterative process in which the model is modified (and hopefully improved) with each iteration of diagnosis and respecification. Once a satisfactory model is found, it should be double checked by applying it to a different data set.
[edit] Types of models
Broadly speaking economic models are stochastic or non-stochastic; discrete or continuous choice.
Stochastic models are formulated using stochastic processes. They model economically observable values over time. Most of econometrics is based on statistics to formulate and test hypotheses about these processes or estimate parameters for them. A widely used class of econometric models popularized by Tinbergen and later Wold are autoregressive models, in which the stochastic process satisfies some relation between current and past values. Examples of these are autoregressive moving average models and related ones such as autoregressive conditional heteroskedasticity (ARCH) and GARCH models for the modelling of heteroskedasticity.
Non-stochastic mathematical models may be purely qualitative (for example, models involved in some aspect of social choice theory) or quantitative (involving rationalization of financial variables, for example with hyperbolic coordinates, and/or specific forms of functional relationships between variables). In some cases economic predictions of a model merely assert the direction of movement of economic variables, and so the functional relationships are used only in a qualitative sense: for example, if the price of an item increases, then the demand for that item will decrease. For such models, economists often use two-dimensional graphs instead of functions.
Qualitative models - Although almost all economic models involve some form of mathematical or quantitative analysis, qualitative models are occasionally used. One example is qualitative scenario planning in which possible future events are played out. Another example is non-numerical decision tree analysis. Qualitative models often suffer from lack of precision.
At a more practical level, quantitative modelling is applied to many areas of economics and several methodologies have evolved more or less independently of each other. As a result, no overall model taxonomy is naturally available. We can nonetheless provide a few examples which illustrate some particularly relevant points of model construction.
An accounting model is one based on the premise that for every credit there is a debit. More symbolically, an accounting model expresses some principle of conservation in the form
algebraic sum of inflows = sinks − sources
This principle is certainly true for money and it is the basis for national income accounting. Accounting models are true by convention, that is any experimental failure to confirm them, would be attributed to fraud, arithmetic error or an extraneous injection (or destruction) of cash which we would interpret as showing the experiment was conducted improperly.
Optimality and constrained optimization models - Other examples of quantitative models are based on principles such as profit or utility maximization. An example of such a model is given by the comparative statics of taxation on the profit-maximizing firm. The profit of a firm is given by
where p(x) is the price that a product commands in the market if it is supplied at the rate x, xp(x) is the revenue obtained from selling the product, C(x) is the cost of bringing the product to market at the rate x, and t is the tax that the firm must pay per unit of the product sold.
The profit maximization assumption states that a firm will produce at the output rate x if that rate maximizes the firm's profit. Using differential calculus we can obtain conditions on x under which this holds. The first order maximization condition for x is
Regarding x is an implicitly defined function of t by this equation (see implicit function theorem), one concludes that the derivative of x with respect to t has the same sign as
which is negative if the second order conditions for a local maximum are satisfied.
Thus the profit maximization model predicts something about the effect of taxation on output, namely that output decreases with increased taxation. If the predictions of the model fail, we conclude that the profit maximization hypothesis was false; this should lead to alternate theories of the firm, for example based on bounded rationality.
Borrowing a notion apparently first used in economics by Paul Samuelson, this model of taxation and the predicted dependency of output on the tax rate, illustrates an operationally meaningful theorem; that is one which requires some economically meaningful assumption which is falsifiable under certain conditions.
Aggregate models. Macroeconomics needs to deal with aggregate quantities such as output, the price level, the interest rate and so on. Now real output is actually a vector of goods and services, such as cars, passenger airplanes, computers, food items, secretarial services, home repair services etc. Similarly price is the vector of individual prices of goods and services. Models in which the vector nature of the quantities is maintained are used in practice, for example Leontief input-output models are of this kind. However, for the most part, these models are computationally much harder to deal with and harder to use as tools for qualitative analysis. For this reason, macroeconomic models usually lump together different variables into a single quantity such as output or price. Moreover, quantitative relationships between these aggregate variables are often parts of important macroeconomic theories. This process of aggregation and functional dependency between various aggregates usually is interpreted statistically and validated by econometrics. For instance, one ingredient of the Keynesian model is a functional relationship between consumption and national income: C = C(Y). This relationship plays an important role in Keynesian analysis.
[edit] Pitfalls
[edit] Restrictive, unrealistic assumptions
Economic models can be such powerful tools in understanding some economic relationships, that it is easy to ignore their limitations.
[edit] Omitted details
This section does not cite any references or sources. Please help improve this article by adding citations to reliable sources. Unverifiable material may be challenged and removed. (September 2007)
This article contains weasel words, vague phrasing that often accompanies biased or unverifiable information. Such statements should be clarified or removed. (December 2007)
A great danger inherent in the simplification required to fit the entire economy into a model is omitting critical elements. Some economists believe that making the model as simple as possible is an art form, but the details left out are often contentious. For instance:
Market models often exclude externalities such as unpunished pollution. Such models are the basis for many environmentalist attacks on mainstream economists. It is said that if the social costs of externalities were included in the models their conclusions would be very different, and models are often accused of leaving out these terms because of economist's pro-free market bias.
In turn, environmental economics has been accused of omitting key financial considerations from its models. For example the returns to solar power investments are sometimes modelled without a discount factor, so that the present utility of solar energy delivered in a century's time is precisely equal to gas-power station energy today.
Financial models can be oversimplified by relying on historically unprecedented arbitrage-free markets, probably underestimating the chance of crises, and under-pricing or under-planning for risk.
Models of consumption either assume that humans are immortal or that teenagers plan their life around an optimal retirement supported by the next generation. (These conclusions are probably harmless, except possibly to the credibility of the modelling profession.)
[edit] Are economic models falsifiable?
The sharp distinction between falsifiable economic models and those that are not is by no means a universally accepted one. Indeed one can argue that the ceteris paribus (all else being equal) qualification that accompanies any claim in economics is nothing more than an all-purpose escape clause (See N. de Marchi and M. Blaug.) The all else being equal claim allows holding all variables constant except the few that the model is attempting to reason about. This allows the separation and clarification of the specific relationship. However, in reality all else is never equal, so economic models are guaranteed to not be perfect. The goal of the model is that the isolated and simplified relationship has some predictive power that can be tested, mainly that it is a theory capable of being applied to reality. To qualify as a theory, a model should arguably answer three questions: Theory of what?, Why should we care?, What merit in your explanation? If the model fails to do so, it is probably too detached from reality and meaningful societal issues to qualify as theory. Research conducted according to this three-question test finds that in the 2004 edition of the Journal of Economic Theory, only 12% of the articles satisfy the three requirements.” [3] Ignoring the fact that the ceteris paribus assumption is being made is another big failure often made when a model is applied. At the minimum an attempt must be made to look at the various factors that may not be equal and take those into account.
[edit] History
One of the major problems addressed by economic models has been understanding economic growth. An early attempt to provide a technique to approach this came from the French physiocratic school in the Eighteenth century. Among these economists, François Quesnay should be noted, particularly for his development and use of tables he called Tableaux économiques. These tables have in fact been interpreted in more modern terminology as a Leontiev model, see the Phillips reference below.
All through the 18th century (that is, well before the founding of modern political economy, conventionally marked by Adam Smith's 1776 Wealth of Nations) simple probabilistic models were used to understand the economics of insurance. This was a natural extrapolation of the theory of gambling, and played an important role both in the development of probability theory itself and in the development of actuarial science. Many of the giants of 18th century mathematics contributed to this field. Around 1730, De Moivre addressed some of these problems in the 3rd edition of the Doctrine of Chances. Even earlier (1709), Nicolas Bernoulli studies problems related to savings and interest in the Ars Conjectandi. In 1730, Daniel Bernoulli studied "moral probability" in his book Mensura Sortis, where he introduced what would today be called "logarithmic utility of money" and applied it to gambling and insurance problems, including a solution of the paradoxical Saint Petersburg problem. All of these developments were summarized by Laplace in his Analytical Theory of Probability (1812). Clearly, by the time David Ricardo came along he had a lot of well-established math to draw from.
[edit] Tests of macroeconomic predictions
In the late 1980s a research institute compared the twelve top macroeconomic models available at the time. They asked the designers of these models what would happen to the economy under a variety of quantitatively specified shocks, and compared the diversity in the answers (allowing the models to control for all the variability in the real world; this was a test of model vs. model, not a test against the actual outcome). Although the designers were allowed to simplify the world and start from a stable, known baseline (e.g NAIRU unemployment) the various models gave dramatically different answers. For instance, in calculating the impact of a monetary loosening on output some models estimated a 3% change in GDP after one year, and one gave almost no change, with the rest spread between.
Partly as a result of such experiments, modern central bankers no longer have as much confidence that it is possible to 'fine-tune' the economy as they had in the 1960s and early 1970s. Modern policy makers tend to use a less activist approach, explicitly because they lack confidence that their models will actually predict where the economy is going, or the effect of any shock upon it. The new, more humble, approach sees danger in dramatic policy changes based on model predictions, because of several practical and theoretical limitations in current macroeconomic models; in addition to the theoretical pitfalls, (listed above) some problems specific to aggregate modelling are:
Limitations in model construction caused by difficulties in understanding the underlying mechanisms of the real economy. (Hence the profusion of separate models.)
The law of Unintended consequences, on elements of the real economy not yet included in the model.
The time lag in both receiving data and the reaction of economic variables to policy makers attempts to 'steer' them (mostly through monetary policy) in the direction that central bankers want them to move. Milton Friedman has vigorously argued that these lags are so long and unpredictably variable that effective management of the macroeconomy is impossible.
The difficulty in correctly specifying all of the parameters (through econometric measurements) even if the structural model and data were perfect.
The fact that all the model's relationships and coefficients are stochastic, so that the error term becomes very large quickly, and the available snapshot of the input parameters is already out of date.
Modern economic models incorporate the reaction of the public & market to the policy maker's actions (through game theory), and this feedback is included in modern models (following the rational expectations revolution and Robert Lucas, Jr.'s critique of the optimal control concept of precise macroeconomic management). If the response to the decision maker's actions (and their credibility) must be included in the model then it becomes much harder to influence some of the variables simulated.
[edit] Comparison with models in other sciences
The comparison of economic forecasting to weather forecasting using (much more sophisticated) simulations shows the present state of economic modelling in an unflattering light.[citation needed] Although meteorological simulations are capable of only about two days reliability, this is all they claim to predict; the medium and long term macroeconomic models presently available often have similar predictive power to 1930s weather forecasters looking five days ahead.[citation needed]
Complex systems specialist and mathematician David Orrell wrote on this issue and explained that the weather, human health and economics use similar methods of prediction (mathematical models). Their systems - the atmosphere, the human body and the economy - also have similar levels of complexity. He found that forecasts fail because the models suffer from two problems : i- they cannot capture the full detail of the underlying system, so rely on approximate equations; ii- they are sensitive to small changes in the exact form of these equations. This is because complex systems like the economy or the climate consist of a delicate balance of opposing forces, so a slight imbalance in their representation has big effects. Thus, predictions of things like economic recessions are still highly inaccurate, despite the use of enormous models running on fast computers. [2]
[edit] The effects of deterministic chaos on economic models
Economic and meteorological simulations may share a fundamental limit to their predictive powers: chaos. Although the modern mathematical work on chaotic systems began in the 1970s the danger of chaos had been identified and defined in Econometrica as early as 1958:
"Good theorising consists to a large extent in avoiding assumptions....(with the property that)....a small change in what is posited will seriously affect the conclusions."
(William Baumol, Econometrica, 26 see: Economics on the Edge of Chaos).
It is straightforward to design an economic models susceptible to butterfly effects of initial-condition sensitivity. See, for instance: Review of chaotic models from 2003
However, the econometric research program to identify which variables are chaotic (if any) has largely concluded that aggregate macroeconomic variables probably do not behave chaotically. This would mean that refinements to the models could ultimately produce reliable long-term forecasts. However the validity of this conclusion has generated two challenges:
In 2004 Philip Mirowski challenged this view and those who hold it, saying that chaos in economics is suffering from a biased "crusade" against it by neo-classical economics in order to preserve their mathematical models.
The variables in finance may well be subject to chaos. Also in 2004, the University of Canterbury study Economics on the Edge of Chaos concludes that after noise is removed from S&P 500 returns, evidence of deterministic chaos is found.
More recently, chaos (or the butterfly effect) has been identified as less significant than previously thought to explain prediction errors. Rather, the predictive power of economics and meteorology would mostly be limited by the models themselves and the nature of their underlying systems (see Comparison with models in other sciences above).
[edit] The critique of hubris in planning
A key strand of free market economic thinking is that the market's "invisible hand" guides an economy to prosperity more efficiently than central planning using an economic model. One reason, emphasized by Friedrich Hayek, is the claim that many of the true forces shaping the economy can never be captured in a single plan. This is an argument which cannot be made through a conventional (mathematical) economic model, because it says that there are critical systemic-elements that will always be omitted from any top-down analysis of the economy.[4]
[edit] Examples of economic models
Black-Scholes option pricing model
Heckscher-Ohlin model
International Futures
IS/LM model
Participatory Economics
Keynesian cross
Leontief's input-output model
World3
Contents
1 Overview
2 Types of models
3 Pitfalls
3.1 Restrictive, unrealistic assumptions
3.2 Omitted details
3.3 Are economic models falsifiable?
4 History
5 Tests of macroeconomic predictions
5.1 Comparison with models in other sciences
5.2 The effects of deterministic chaos on economic models
5.3 The critique of hubris in planning
6 Examples of economic models
7 See also
8 Notes
9 References
10 External links
[edit] Overview
In general terms, economic models have two functions: first as a simplification of and abstraction from observed data, and second as a means of selection of data based on a paradigm of econometric study.
Simplification is particularly important for economics given the enormous complexity of economic processes. This complexity can be attributed to the diversity of factors that determine economic activity; these factors include: individual and cooperative decision processes, resource limitations, environmental and geographical constraints, institutional and legal requirements and purely random fluctuations. Economists therefore must make a reasoned choice of which variables and which relationships between these variables are relevant and which ways of analyzing and presenting this information are useful.
Selection is important because the nature of an economic model will often determine what facts will be looked at, and how they will be compiled. For example inflation is a general economic concept, but to measure inflation requires a model of behavior, so that an economist can differentiate between real changes in price, and changes in price which are to be attributed to inflation.
In addition to their professional academic interest, the use of models include:
Forecasting economic activity in a way in which conclusions are logically related to assumptions;
Proposing economic policy to modify future economic activity;
Presenting reasoned arguments to politically justify economic policy at the national level, to explain and influence company strategy at the level of the firm, or to provide intelligent advice for household economic decisions at the level of households.
Planning and allocation, in the case of centrally planned economies, and on a smaller scale in logistics and management of businesses.
In finance predictive models have been used since the 1980s for trading (investment, and speculation), for example emerging market bonds were often traded based on economic models predicting the growth of the developing nation issuing them. Since the 1990s many long-term risk management models have incorporated economic relationships between simulated variables in an attempt to detect high-exposure future scenarios (often through a Monte Carlo method).
Obviously any kind of reasoning about anything uses representations by variables and logical relationships. A model however establishes an argumentative framework for applying logic and mathematics that can be independently discussed and tested and that can be applied in various instances. Policies and arguments that rely on economic models have a clear basis for soundness, namely the validity of the supporting model.
Economic models in current use do not pretend to be theories of everything economic; any such pretensions would immediately be thwarted by computational infeasibility and the paucity of theories for most types of economic behavior. Therefore conclusions drawn from models will be approximate representations of economic facts. However, properly constructed models can remove extraneous information and isolate useful approximations of key relationships. In this way more can be understood about the relationships in question than by trying to understand the entire economic process.
The details of model construction vary with type of model and its application, but a generic process can be identified. Generally any modelling process has two steps: generating a model, then checking the model for accuracy (sometimes called diagnostics). The diagnostic step is important because a model is only useful to the extent that it accurately mirrors the relationships that it purports to describe. Creating and diagnosing a model is frequently an iterative process in which the model is modified (and hopefully improved) with each iteration of diagnosis and respecification. Once a satisfactory model is found, it should be double checked by applying it to a different data set.
[edit] Types of models
Broadly speaking economic models are stochastic or non-stochastic; discrete or continuous choice.
Stochastic models are formulated using stochastic processes. They model economically observable values over time. Most of econometrics is based on statistics to formulate and test hypotheses about these processes or estimate parameters for them. A widely used class of econometric models popularized by Tinbergen and later Wold are autoregressive models, in which the stochastic process satisfies some relation between current and past values. Examples of these are autoregressive moving average models and related ones such as autoregressive conditional heteroskedasticity (ARCH) and GARCH models for the modelling of heteroskedasticity.
Non-stochastic mathematical models may be purely qualitative (for example, models involved in some aspect of social choice theory) or quantitative (involving rationalization of financial variables, for example with hyperbolic coordinates, and/or specific forms of functional relationships between variables). In some cases economic predictions of a model merely assert the direction of movement of economic variables, and so the functional relationships are used only in a qualitative sense: for example, if the price of an item increases, then the demand for that item will decrease. For such models, economists often use two-dimensional graphs instead of functions.
Qualitative models - Although almost all economic models involve some form of mathematical or quantitative analysis, qualitative models are occasionally used. One example is qualitative scenario planning in which possible future events are played out. Another example is non-numerical decision tree analysis. Qualitative models often suffer from lack of precision.
At a more practical level, quantitative modelling is applied to many areas of economics and several methodologies have evolved more or less independently of each other. As a result, no overall model taxonomy is naturally available. We can nonetheless provide a few examples which illustrate some particularly relevant points of model construction.
An accounting model is one based on the premise that for every credit there is a debit. More symbolically, an accounting model expresses some principle of conservation in the form
algebraic sum of inflows = sinks − sources
This principle is certainly true for money and it is the basis for national income accounting. Accounting models are true by convention, that is any experimental failure to confirm them, would be attributed to fraud, arithmetic error or an extraneous injection (or destruction) of cash which we would interpret as showing the experiment was conducted improperly.
Optimality and constrained optimization models - Other examples of quantitative models are based on principles such as profit or utility maximization. An example of such a model is given by the comparative statics of taxation on the profit-maximizing firm. The profit of a firm is given by
where p(x) is the price that a product commands in the market if it is supplied at the rate x, xp(x) is the revenue obtained from selling the product, C(x) is the cost of bringing the product to market at the rate x, and t is the tax that the firm must pay per unit of the product sold.
The profit maximization assumption states that a firm will produce at the output rate x if that rate maximizes the firm's profit. Using differential calculus we can obtain conditions on x under which this holds. The first order maximization condition for x is
Regarding x is an implicitly defined function of t by this equation (see implicit function theorem), one concludes that the derivative of x with respect to t has the same sign as
which is negative if the second order conditions for a local maximum are satisfied.
Thus the profit maximization model predicts something about the effect of taxation on output, namely that output decreases with increased taxation. If the predictions of the model fail, we conclude that the profit maximization hypothesis was false; this should lead to alternate theories of the firm, for example based on bounded rationality.
Borrowing a notion apparently first used in economics by Paul Samuelson, this model of taxation and the predicted dependency of output on the tax rate, illustrates an operationally meaningful theorem; that is one which requires some economically meaningful assumption which is falsifiable under certain conditions.
Aggregate models. Macroeconomics needs to deal with aggregate quantities such as output, the price level, the interest rate and so on. Now real output is actually a vector of goods and services, such as cars, passenger airplanes, computers, food items, secretarial services, home repair services etc. Similarly price is the vector of individual prices of goods and services. Models in which the vector nature of the quantities is maintained are used in practice, for example Leontief input-output models are of this kind. However, for the most part, these models are computationally much harder to deal with and harder to use as tools for qualitative analysis. For this reason, macroeconomic models usually lump together different variables into a single quantity such as output or price. Moreover, quantitative relationships between these aggregate variables are often parts of important macroeconomic theories. This process of aggregation and functional dependency between various aggregates usually is interpreted statistically and validated by econometrics. For instance, one ingredient of the Keynesian model is a functional relationship between consumption and national income: C = C(Y). This relationship plays an important role in Keynesian analysis.
[edit] Pitfalls
[edit] Restrictive, unrealistic assumptions
Economic models can be such powerful tools in understanding some economic relationships, that it is easy to ignore their limitations.
[edit] Omitted details
This section does not cite any references or sources. Please help improve this article by adding citations to reliable sources. Unverifiable material may be challenged and removed. (September 2007)
This article contains weasel words, vague phrasing that often accompanies biased or unverifiable information. Such statements should be clarified or removed. (December 2007)
A great danger inherent in the simplification required to fit the entire economy into a model is omitting critical elements. Some economists believe that making the model as simple as possible is an art form, but the details left out are often contentious. For instance:
Market models often exclude externalities such as unpunished pollution. Such models are the basis for many environmentalist attacks on mainstream economists. It is said that if the social costs of externalities were included in the models their conclusions would be very different, and models are often accused of leaving out these terms because of economist's pro-free market bias.
In turn, environmental economics has been accused of omitting key financial considerations from its models. For example the returns to solar power investments are sometimes modelled without a discount factor, so that the present utility of solar energy delivered in a century's time is precisely equal to gas-power station energy today.
Financial models can be oversimplified by relying on historically unprecedented arbitrage-free markets, probably underestimating the chance of crises, and under-pricing or under-planning for risk.
Models of consumption either assume that humans are immortal or that teenagers plan their life around an optimal retirement supported by the next generation. (These conclusions are probably harmless, except possibly to the credibility of the modelling profession.)
[edit] Are economic models falsifiable?
The sharp distinction between falsifiable economic models and those that are not is by no means a universally accepted one. Indeed one can argue that the ceteris paribus (all else being equal) qualification that accompanies any claim in economics is nothing more than an all-purpose escape clause (See N. de Marchi and M. Blaug.) The all else being equal claim allows holding all variables constant except the few that the model is attempting to reason about. This allows the separation and clarification of the specific relationship. However, in reality all else is never equal, so economic models are guaranteed to not be perfect. The goal of the model is that the isolated and simplified relationship has some predictive power that can be tested, mainly that it is a theory capable of being applied to reality. To qualify as a theory, a model should arguably answer three questions: Theory of what?, Why should we care?, What merit in your explanation? If the model fails to do so, it is probably too detached from reality and meaningful societal issues to qualify as theory. Research conducted according to this three-question test finds that in the 2004 edition of the Journal of Economic Theory, only 12% of the articles satisfy the three requirements.” [3] Ignoring the fact that the ceteris paribus assumption is being made is another big failure often made when a model is applied. At the minimum an attempt must be made to look at the various factors that may not be equal and take those into account.
[edit] History
One of the major problems addressed by economic models has been understanding economic growth. An early attempt to provide a technique to approach this came from the French physiocratic school in the Eighteenth century. Among these economists, François Quesnay should be noted, particularly for his development and use of tables he called Tableaux économiques. These tables have in fact been interpreted in more modern terminology as a Leontiev model, see the Phillips reference below.
All through the 18th century (that is, well before the founding of modern political economy, conventionally marked by Adam Smith's 1776 Wealth of Nations) simple probabilistic models were used to understand the economics of insurance. This was a natural extrapolation of the theory of gambling, and played an important role both in the development of probability theory itself and in the development of actuarial science. Many of the giants of 18th century mathematics contributed to this field. Around 1730, De Moivre addressed some of these problems in the 3rd edition of the Doctrine of Chances. Even earlier (1709), Nicolas Bernoulli studies problems related to savings and interest in the Ars Conjectandi. In 1730, Daniel Bernoulli studied "moral probability" in his book Mensura Sortis, where he introduced what would today be called "logarithmic utility of money" and applied it to gambling and insurance problems, including a solution of the paradoxical Saint Petersburg problem. All of these developments were summarized by Laplace in his Analytical Theory of Probability (1812). Clearly, by the time David Ricardo came along he had a lot of well-established math to draw from.
[edit] Tests of macroeconomic predictions
In the late 1980s a research institute compared the twelve top macroeconomic models available at the time. They asked the designers of these models what would happen to the economy under a variety of quantitatively specified shocks, and compared the diversity in the answers (allowing the models to control for all the variability in the real world; this was a test of model vs. model, not a test against the actual outcome). Although the designers were allowed to simplify the world and start from a stable, known baseline (e.g NAIRU unemployment) the various models gave dramatically different answers. For instance, in calculating the impact of a monetary loosening on output some models estimated a 3% change in GDP after one year, and one gave almost no change, with the rest spread between.
Partly as a result of such experiments, modern central bankers no longer have as much confidence that it is possible to 'fine-tune' the economy as they had in the 1960s and early 1970s. Modern policy makers tend to use a less activist approach, explicitly because they lack confidence that their models will actually predict where the economy is going, or the effect of any shock upon it. The new, more humble, approach sees danger in dramatic policy changes based on model predictions, because of several practical and theoretical limitations in current macroeconomic models; in addition to the theoretical pitfalls, (listed above) some problems specific to aggregate modelling are:
Limitations in model construction caused by difficulties in understanding the underlying mechanisms of the real economy. (Hence the profusion of separate models.)
The law of Unintended consequences, on elements of the real economy not yet included in the model.
The time lag in both receiving data and the reaction of economic variables to policy makers attempts to 'steer' them (mostly through monetary policy) in the direction that central bankers want them to move. Milton Friedman has vigorously argued that these lags are so long and unpredictably variable that effective management of the macroeconomy is impossible.
The difficulty in correctly specifying all of the parameters (through econometric measurements) even if the structural model and data were perfect.
The fact that all the model's relationships and coefficients are stochastic, so that the error term becomes very large quickly, and the available snapshot of the input parameters is already out of date.
Modern economic models incorporate the reaction of the public & market to the policy maker's actions (through game theory), and this feedback is included in modern models (following the rational expectations revolution and Robert Lucas, Jr.'s critique of the optimal control concept of precise macroeconomic management). If the response to the decision maker's actions (and their credibility) must be included in the model then it becomes much harder to influence some of the variables simulated.
[edit] Comparison with models in other sciences
The comparison of economic forecasting to weather forecasting using (much more sophisticated) simulations shows the present state of economic modelling in an unflattering light.[citation needed] Although meteorological simulations are capable of only about two days reliability, this is all they claim to predict; the medium and long term macroeconomic models presently available often have similar predictive power to 1930s weather forecasters looking five days ahead.[citation needed]
Complex systems specialist and mathematician David Orrell wrote on this issue and explained that the weather, human health and economics use similar methods of prediction (mathematical models). Their systems - the atmosphere, the human body and the economy - also have similar levels of complexity. He found that forecasts fail because the models suffer from two problems : i- they cannot capture the full detail of the underlying system, so rely on approximate equations; ii- they are sensitive to small changes in the exact form of these equations. This is because complex systems like the economy or the climate consist of a delicate balance of opposing forces, so a slight imbalance in their representation has big effects. Thus, predictions of things like economic recessions are still highly inaccurate, despite the use of enormous models running on fast computers. [2]
[edit] The effects of deterministic chaos on economic models
Economic and meteorological simulations may share a fundamental limit to their predictive powers: chaos. Although the modern mathematical work on chaotic systems began in the 1970s the danger of chaos had been identified and defined in Econometrica as early as 1958:
"Good theorising consists to a large extent in avoiding assumptions....(with the property that)....a small change in what is posited will seriously affect the conclusions."
(William Baumol, Econometrica, 26 see: Economics on the Edge of Chaos).
It is straightforward to design an economic models susceptible to butterfly effects of initial-condition sensitivity. See, for instance: Review of chaotic models from 2003
However, the econometric research program to identify which variables are chaotic (if any) has largely concluded that aggregate macroeconomic variables probably do not behave chaotically. This would mean that refinements to the models could ultimately produce reliable long-term forecasts. However the validity of this conclusion has generated two challenges:
In 2004 Philip Mirowski challenged this view and those who hold it, saying that chaos in economics is suffering from a biased "crusade" against it by neo-classical economics in order to preserve their mathematical models.
The variables in finance may well be subject to chaos. Also in 2004, the University of Canterbury study Economics on the Edge of Chaos concludes that after noise is removed from S&P 500 returns, evidence of deterministic chaos is found.
More recently, chaos (or the butterfly effect) has been identified as less significant than previously thought to explain prediction errors. Rather, the predictive power of economics and meteorology would mostly be limited by the models themselves and the nature of their underlying systems (see Comparison with models in other sciences above).
[edit] The critique of hubris in planning
A key strand of free market economic thinking is that the market's "invisible hand" guides an economy to prosperity more efficiently than central planning using an economic model. One reason, emphasized by Friedrich Hayek, is the claim that many of the true forces shaping the economy can never be captured in a single plan. This is an argument which cannot be made through a conventional (mathematical) economic model, because it says that there are critical systemic-elements that will always be omitted from any top-down analysis of the economy.[4]
[edit] Examples of economic models
Black-Scholes option pricing model
Heckscher-Ohlin model
International Futures
IS/LM model
Participatory Economics
Keynesian cross
Leontief's input-output model
World3
Homo Economics
Homo economicus, or Economic human, is the concept in some economic theories of humans as rational and broadly self-interested actors who have the ability to make judgments towards their subjectively defined ends.
Contents
1 History of the term
2 Model
3 Criticisms
4 Responses
5 Homo sociologicus
6 See also
7 References
8 External links
[edit] History of the term
The term "Economic Man" was used for the first time in the late nineteenth century by critics of John Stuart Mill’s work on political economy.[1][2] Below is a passage from Mill’s work that those 19th-century critics were referring to:
"[Political economy] does not treat the whole of man’s nature as modified by the social state, nor of the whole conduct of man in society. It is concerned with him solely as a being who desires to possess wealth, and who is capable of judging the comparative efficacy of means for obtaining that end."[3]
Later in the same work, Mill goes on to write that he is proposing “an arbitrary definition of man, as a being who inevitably does that by which he may obtain the greatest amount of necessaries, conveniences, and luxuries, with the smallest quantity of labour and physical self-denial with which they can be obtained.”
Although the term did not come into use until the 19th century, it is often associated with the ideas of 18th century thinkers like Adam Smith and David Ricardo. In The Wealth of Nations, Smith wrote:
"It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own interest."[4]
This suggests the same sort of rational, self-interested, labor-averse individual that Mill proposes (although Smith did claim that individuals have sympathy for the well-being of others, in The Theory of Moral Sentiments). Aristotle's Politics discussed the nature of self interest in Book II, Part V.
"Again, how immeasurably greater is the pleasure, when a man feels a thing to be his own; for surely the love of self is a feeling implanted by nature and not given in vain, although selfishness is rightly censured; this, however, is not the mere love of self, but the love of self in excess, like the miser's love of money; for all, or almost all, men love money and other such objects in a measure. And further, there is the greatest pleasure in doing a kindness or service to friends or guests or companions, which can only be rendered when a man has private property."
A wave of economists in the late 19th century—Francis Edgeworth, William Stanley Jevons, Leon Walras, and Vilfredo Pareto—built mathematical models on these assumptions. In the 20th century, Lionel Robbins’ rational choice theory came to dominate mainstream economics and the term Economic Man took on a more specific meaning of a person who acted rationally on complete knowledge out of self-interest and the desire for wealth.
[edit] Model
Homo economicus is a term used for an approximation or model of Homo sapiens that acts to obtain the highest possible well-being for himself given available information about opportunities and other constraints, both natural and institutional, on his ability to achieve his predetermined goals. This approach has been formalized in certain social science models, particularly in economics.
Homo economicus is seen as "rational" in the sense that well-being as defined by the utility function is optimized given perceived opportunities. That is, the individual seeks to attain very specific and predetermined goals to the greatest extent with the least possible cost. Note that this kind of "rationality" does not say that the individual's actual goals are "rational" in some larger ethical, social, or human sense, only that he tries to attain them at minimal cost. Only naïve applications of the Homo economicus model assume that this hypothetical individual knows what is best for his long-term physical and mental health and can be relied upon to always make the right decision for himself. See rational choice theory and rational expectations for further discussion; the article on rationality widens the discussion.
As in social science in general, these assumptions are at best approximations. The term is often used derogatorily in academic literature, perhaps most commonly by sociologists, many of whom tend to prefer structural explanations to ones based on rational action by individuals.
The use of the Latin form Homo economicus is certainly long established; Persky[1] traces it back to Pareto (1906)[5] but notes that it may be older. The English term economic man can be found even earlier, in John Kells Ingram's A History of Political Economy (1888).[6] The Oxford English Dictionary (O.E.D.) does not mention Homo economicus, but it is one of a number of phrases that imitate the scientific name for the human species. According to the O.E.D., the human genus name Homo is
Used with L. or mock-L. adjs. in names imitating Homo sapiens, etc., and intended to personify some aspect of human life or behaviour (indicated by the adj.). Homo faber ("feIb@(r)) [H. Bergson L'Evolution Créatrice (1907) ii. 151], a term used to designate man as a maker of tools.) Variants are often comic: Homo insipiens; Homo turisticus. (This is from the CD edition of 2002.)
Note that such forms should logically keep the capital for the "genus" name—i.e., Homo economicus rather than homo economicus. Actual usage is inconsistent.
[edit] Criticisms
Homo economicus bases his choices on a consideration of his own personal "utility function".
Consequently, the "homo economicus" assumptions have been criticized not only by economists on the basis of logical arguments, but also on empirical grounds by cross-cultural comparison. Economic anthropologists such as Marshall Sahlins[7], Karl Polanyi[8], Marcel Mauss[9] or Maurice Godelier[10] have demonstrated that in traditional societies, choices people make regarding production and exchange of goods follow patterns of reciprocity which differ sharply from what the "homo economicus" model postulates. Such systems have been termed gift economy rather than market economy. Criticisms of the "homo economicus" model put forward from the standpoint of ethics usually refer to thís traditional ethic of kinship-based reciprocity that held together traditional societies.
Economists Thorstein Veblen, John Maynard Keynes, Herbert Simon, and many of the Austrian School criticise Homo economicus as an actor with too great of an understanding of macroeconomics and economic forecasting in his decision making. They stress uncertainty and bounded rationality in the making of economic decisions, rather than relying on the rational man who is fully informed of all circumstances impinging on his decisions. They argue that perfect knowledge never exists, which means that all economic activity implies risk.
Empirical studies by Amos Tversky questioned the assumption that investors are rational. In 1995, Tversky demonstrated the tendency of investors to make risk-averse choices in gains, and risk-seeking choices in losses. The investors appeared as very risk-averse for small losses but indifferent for a small chance of a very large loss. This violates economic rationality as usually understood. Further research on this subject, showing other deviations from conventionally-defined economic rationality, is being done in the growing field of experimental or behavioral economics. Some of the broader issues involved in this criticism are studied in Decision Theory of which Rational Choice Theory is only a subset.
Other critics of the Homo economicus model of humanity, such as Bruno Frey, point to the excessive emphasis on extrinsic motivation (rewards and punishments from the social environment) as opposed to intrinsic motivation. For example, it is difficult if not impossible to understand how Homo economicus would be a hero in war or would get inherent pleasure from craftsmanship. Frey and others argue that too much emphasis on rewards and punishments can "crowd out" (discourage) intrinsic motivation: paying a boy for doing household tasks may push him from doing those tasks "to help the family" to doing them simply for the reward.
Another weakness is highlighted by sociologists, who argue that Homo economicus ignores an extremely important question, i.e., the origins of tastes and the parameters of the utility function by social influences, training, education, and the like. The exogeneity of tastes (preferences) in this model is the major distinction from Homo sociologicus, in which tastes are taken as partially or even totally determined by the societal environment (see below).
Further critics, learning from the broadly-defined psychoanalytic tradition, criticize the Homo economicus model as ignoring the inner conflicts that real-world individuals suffer, as between short-term and long-term goals (e.g., eating chocolate cake and losing weight) or between individual goals and societal values. Such conflicts may lead to "irrational" behavior involving inconsistency, psychological paralysis, neurosis, and/or psychic pain.
Some critics argue that a "naive" presentation of Homo Economicus model can result in a self-fulfilling prophecy. One possible case of this has been in the teaching of economics. Several research studies have indicated that those students who take economics courses end up being more self-centered than before they took the courses. For example, they are less willing to co-operate with the other player in a "prisoner's-dilemma"-type game.[11]
[edit] Responses
This article does not cite any references or sources. Please help improve this article by adding citations to reliable sources. Unverifiable material may be challenged and removed. (December 2008)
Economists tend to disagree with these critiques, arguing that it may be relevant to analyze the consequences of enlightened egoism just as it may be worthwhile to consider altruistic or social behavior. Others argue that we need to understand the consequences of such narrow-minded greed even if only a small percentage of the population embraces such motives. Free riders, for example, would have a major negative impact on the provision of public goods. However, economists' supply and demand predictions might obtain even if only a significant minority of market participants act like Homo economicus. In this view, the assumption of Homo economicus can and should be simply a preliminary step on the road to a more sophisticated model.
Yet others argue that Homo economicus is a reasonable approximation for behavior within market institutions, since the individualized nature of human action in such social settings encourages individualistic behavior. Not only do market settings encourage the application of a simple cost/benefit calculus by individuals, but they reward and thus attract the more individualistic people. It can be difficult to apply social values (as opposed to following self-interest) in an extremely competitive market; a company that refuses to pollute (for example) may find itself bankrupt.
Defenders of the Homo economicus model see many critics of the dominant school as using a straw-man technique. For example, it is common for critics to argue that real people do not have cost-less access to infinite information and an innate ability to instantly process it. However, in advanced-level theoretical economics, scholars have found ways of addressing these problems, modifying models enough to more realistically depict real-life decision-making. For example, models of individual behavior under bounded rationality and of people suffering from envy can be found in the literature. It is primarily when targeting the limiting assumptions made in constructing undergraduate models that the criticisms listed above are valid. These criticisms are especially valid to the extent that the professor asserts that the simplifying assumptions are true and/or uses them in a propagandistic way.
The more sophisticated economists are quite conscious of the empirical limitations of the Homo economicus model. In theory, the views of the critics can be combined with the Homo economicus model to attain a more accurate model.
[edit] Homo sociologicus
Comparisons between economics and sociology have resulted in a corresponding term Homo sociologicus (introduced by German Sociologist Ralf Dahrendorf in 1958), to parody the image of human nature given in some sociological models that attempt to limit the social forces that determine individual tastes and social values. (The alternative or additional source of these would be biology.) Hirsch et al. say that Homo sociologicus is largely a tabula rasa upon which societies and cultures write values and goals; unlike economicus, sociologicus acts not to pursue selfish interests but to fulfill social roles[12] (though the fulfillment of social roles may have a selfish rationale—e.g. politicians or socialites). This "individual" may appear to be all society and no individual.
[edit] See also
Agent (economics)
Rational agent
Rational choice theory
Economic rationalism
List of names for human
Modern portfolio theory
Pirate game
Post-autistic economics
Rational pricing
Homo biologicus
Contents
1 History of the term
2 Model
3 Criticisms
4 Responses
5 Homo sociologicus
6 See also
7 References
8 External links
[edit] History of the term
The term "Economic Man" was used for the first time in the late nineteenth century by critics of John Stuart Mill’s work on political economy.[1][2] Below is a passage from Mill’s work that those 19th-century critics were referring to:
"[Political economy] does not treat the whole of man’s nature as modified by the social state, nor of the whole conduct of man in society. It is concerned with him solely as a being who desires to possess wealth, and who is capable of judging the comparative efficacy of means for obtaining that end."[3]
Later in the same work, Mill goes on to write that he is proposing “an arbitrary definition of man, as a being who inevitably does that by which he may obtain the greatest amount of necessaries, conveniences, and luxuries, with the smallest quantity of labour and physical self-denial with which they can be obtained.”
Although the term did not come into use until the 19th century, it is often associated with the ideas of 18th century thinkers like Adam Smith and David Ricardo. In The Wealth of Nations, Smith wrote:
"It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own interest."[4]
This suggests the same sort of rational, self-interested, labor-averse individual that Mill proposes (although Smith did claim that individuals have sympathy for the well-being of others, in The Theory of Moral Sentiments). Aristotle's Politics discussed the nature of self interest in Book II, Part V.
"Again, how immeasurably greater is the pleasure, when a man feels a thing to be his own; for surely the love of self is a feeling implanted by nature and not given in vain, although selfishness is rightly censured; this, however, is not the mere love of self, but the love of self in excess, like the miser's love of money; for all, or almost all, men love money and other such objects in a measure. And further, there is the greatest pleasure in doing a kindness or service to friends or guests or companions, which can only be rendered when a man has private property."
A wave of economists in the late 19th century—Francis Edgeworth, William Stanley Jevons, Leon Walras, and Vilfredo Pareto—built mathematical models on these assumptions. In the 20th century, Lionel Robbins’ rational choice theory came to dominate mainstream economics and the term Economic Man took on a more specific meaning of a person who acted rationally on complete knowledge out of self-interest and the desire for wealth.
[edit] Model
Homo economicus is a term used for an approximation or model of Homo sapiens that acts to obtain the highest possible well-being for himself given available information about opportunities and other constraints, both natural and institutional, on his ability to achieve his predetermined goals. This approach has been formalized in certain social science models, particularly in economics.
Homo economicus is seen as "rational" in the sense that well-being as defined by the utility function is optimized given perceived opportunities. That is, the individual seeks to attain very specific and predetermined goals to the greatest extent with the least possible cost. Note that this kind of "rationality" does not say that the individual's actual goals are "rational" in some larger ethical, social, or human sense, only that he tries to attain them at minimal cost. Only naïve applications of the Homo economicus model assume that this hypothetical individual knows what is best for his long-term physical and mental health and can be relied upon to always make the right decision for himself. See rational choice theory and rational expectations for further discussion; the article on rationality widens the discussion.
As in social science in general, these assumptions are at best approximations. The term is often used derogatorily in academic literature, perhaps most commonly by sociologists, many of whom tend to prefer structural explanations to ones based on rational action by individuals.
The use of the Latin form Homo economicus is certainly long established; Persky[1] traces it back to Pareto (1906)[5] but notes that it may be older. The English term economic man can be found even earlier, in John Kells Ingram's A History of Political Economy (1888).[6] The Oxford English Dictionary (O.E.D.) does not mention Homo economicus, but it is one of a number of phrases that imitate the scientific name for the human species. According to the O.E.D., the human genus name Homo is
Used with L. or mock-L. adjs. in names imitating Homo sapiens, etc., and intended to personify some aspect of human life or behaviour (indicated by the adj.). Homo faber ("feIb@(r)) [H. Bergson L'Evolution Créatrice (1907) ii. 151], a term used to designate man as a maker of tools.) Variants are often comic: Homo insipiens; Homo turisticus. (This is from the CD edition of 2002.)
Note that such forms should logically keep the capital for the "genus" name—i.e., Homo economicus rather than homo economicus. Actual usage is inconsistent.
[edit] Criticisms
Homo economicus bases his choices on a consideration of his own personal "utility function".
Consequently, the "homo economicus" assumptions have been criticized not only by economists on the basis of logical arguments, but also on empirical grounds by cross-cultural comparison. Economic anthropologists such as Marshall Sahlins[7], Karl Polanyi[8], Marcel Mauss[9] or Maurice Godelier[10] have demonstrated that in traditional societies, choices people make regarding production and exchange of goods follow patterns of reciprocity which differ sharply from what the "homo economicus" model postulates. Such systems have been termed gift economy rather than market economy. Criticisms of the "homo economicus" model put forward from the standpoint of ethics usually refer to thís traditional ethic of kinship-based reciprocity that held together traditional societies.
Economists Thorstein Veblen, John Maynard Keynes, Herbert Simon, and many of the Austrian School criticise Homo economicus as an actor with too great of an understanding of macroeconomics and economic forecasting in his decision making. They stress uncertainty and bounded rationality in the making of economic decisions, rather than relying on the rational man who is fully informed of all circumstances impinging on his decisions. They argue that perfect knowledge never exists, which means that all economic activity implies risk.
Empirical studies by Amos Tversky questioned the assumption that investors are rational. In 1995, Tversky demonstrated the tendency of investors to make risk-averse choices in gains, and risk-seeking choices in losses. The investors appeared as very risk-averse for small losses but indifferent for a small chance of a very large loss. This violates economic rationality as usually understood. Further research on this subject, showing other deviations from conventionally-defined economic rationality, is being done in the growing field of experimental or behavioral economics. Some of the broader issues involved in this criticism are studied in Decision Theory of which Rational Choice Theory is only a subset.
Other critics of the Homo economicus model of humanity, such as Bruno Frey, point to the excessive emphasis on extrinsic motivation (rewards and punishments from the social environment) as opposed to intrinsic motivation. For example, it is difficult if not impossible to understand how Homo economicus would be a hero in war or would get inherent pleasure from craftsmanship. Frey and others argue that too much emphasis on rewards and punishments can "crowd out" (discourage) intrinsic motivation: paying a boy for doing household tasks may push him from doing those tasks "to help the family" to doing them simply for the reward.
Another weakness is highlighted by sociologists, who argue that Homo economicus ignores an extremely important question, i.e., the origins of tastes and the parameters of the utility function by social influences, training, education, and the like. The exogeneity of tastes (preferences) in this model is the major distinction from Homo sociologicus, in which tastes are taken as partially or even totally determined by the societal environment (see below).
Further critics, learning from the broadly-defined psychoanalytic tradition, criticize the Homo economicus model as ignoring the inner conflicts that real-world individuals suffer, as between short-term and long-term goals (e.g., eating chocolate cake and losing weight) or between individual goals and societal values. Such conflicts may lead to "irrational" behavior involving inconsistency, psychological paralysis, neurosis, and/or psychic pain.
Some critics argue that a "naive" presentation of Homo Economicus model can result in a self-fulfilling prophecy. One possible case of this has been in the teaching of economics. Several research studies have indicated that those students who take economics courses end up being more self-centered than before they took the courses. For example, they are less willing to co-operate with the other player in a "prisoner's-dilemma"-type game.[11]
[edit] Responses
This article does not cite any references or sources. Please help improve this article by adding citations to reliable sources. Unverifiable material may be challenged and removed. (December 2008)
Economists tend to disagree with these critiques, arguing that it may be relevant to analyze the consequences of enlightened egoism just as it may be worthwhile to consider altruistic or social behavior. Others argue that we need to understand the consequences of such narrow-minded greed even if only a small percentage of the population embraces such motives. Free riders, for example, would have a major negative impact on the provision of public goods. However, economists' supply and demand predictions might obtain even if only a significant minority of market participants act like Homo economicus. In this view, the assumption of Homo economicus can and should be simply a preliminary step on the road to a more sophisticated model.
Yet others argue that Homo economicus is a reasonable approximation for behavior within market institutions, since the individualized nature of human action in such social settings encourages individualistic behavior. Not only do market settings encourage the application of a simple cost/benefit calculus by individuals, but they reward and thus attract the more individualistic people. It can be difficult to apply social values (as opposed to following self-interest) in an extremely competitive market; a company that refuses to pollute (for example) may find itself bankrupt.
Defenders of the Homo economicus model see many critics of the dominant school as using a straw-man technique. For example, it is common for critics to argue that real people do not have cost-less access to infinite information and an innate ability to instantly process it. However, in advanced-level theoretical economics, scholars have found ways of addressing these problems, modifying models enough to more realistically depict real-life decision-making. For example, models of individual behavior under bounded rationality and of people suffering from envy can be found in the literature. It is primarily when targeting the limiting assumptions made in constructing undergraduate models that the criticisms listed above are valid. These criticisms are especially valid to the extent that the professor asserts that the simplifying assumptions are true and/or uses them in a propagandistic way.
The more sophisticated economists are quite conscious of the empirical limitations of the Homo economicus model. In theory, the views of the critics can be combined with the Homo economicus model to attain a more accurate model.
[edit] Homo sociologicus
Comparisons between economics and sociology have resulted in a corresponding term Homo sociologicus (introduced by German Sociologist Ralf Dahrendorf in 1958), to parody the image of human nature given in some sociological models that attempt to limit the social forces that determine individual tastes and social values. (The alternative or additional source of these would be biology.) Hirsch et al. say that Homo sociologicus is largely a tabula rasa upon which societies and cultures write values and goals; unlike economicus, sociologicus acts not to pursue selfish interests but to fulfill social roles[12] (though the fulfillment of social roles may have a selfish rationale—e.g. politicians or socialites). This "individual" may appear to be all society and no individual.
[edit] See also
Agent (economics)
Rational agent
Rational choice theory
Economic rationalism
List of names for human
Modern portfolio theory
Pirate game
Post-autistic economics
Rational pricing
Homo biologicus
Bounded Rationality
Bounded rationality is a concept based on the fact that rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make decisions. This contrasts with the concept of rationality as optimization.[1] Another way to look at bounded rationality is that, because decision-makers lack the ability and resources to arrive at the optimal solution, they instead apply their rationality only after having greatly simplified the choices available. Thus the decision-maker is a satisficer, one seeking a satisfactory solution rather than the optimal one.[2]
Some models of human behavior in the social sciences assume that humans can be reasonably approximated or described as "rational" entities (see for example rational choice theory). Many economics models assume that people are on average rational, and can in large enough quantities be approximated to act according to their preferences. The concept of bounded rationality revises this assumption to account for the fact that perfectly rational decisions are often not feasible in practice due to the finite computational resources available for making them.
The term is thought to have been coined by Herbert Simon. In Models of Man, Mr. Simon points out that most people are only partly rational, and are in fact emotional/irrational in the remaining part of their actions. In another work, he states "boundedly rational agents experience limits in formulating and solving complex problems and in processing (receiving, storing, retrieving, transmitting) information" (Williamson, p. 553, citing Simon). Simon describes a number of dimensions along which "classical" models of rationality can be made somewhat more realistic, while sticking within the vein of fairly rigorous formalization. These include:
limiting what sorts of utility functions there might be.
recognizing the costs of gathering and processing information.
the possibility of having a "vector" or "multi-valued" utility function.
Simon suggests that economic agents employ the use of heuristics to make decisions rather than a strict rigid rule of optimization. They do this because of the complexity of the situation, and their inability to process and compute the expected utility of every alternative action. Deliberation costs might be high and there are often other, concurrent economic activities also requiring decisions.
Daniel Kahneman proposes bounded rationality as a model to overcome some of the limitations of the rational-agent models in economic literature.
As decision makers have to make decisions about how and when to decide, Ariel Rubinstein proposed to model bounded rationality by explicitly specifying decision-making procedures. This puts the study of decision procedures on the research agenda.
Gerd Gigerenzer argues that most decision theorists who have discussed bounded rationality have not really followed Simon's ideas about it. Rather, they have either considered how people's decisions might be made sub-optimal by the limitations of human rationality, or have constructed elaborate optimising models of how people might cope with their inability to optimize. Gigerenzer instead proposes to examine simple alternatives to a full rationality analysis as a mechanism for decision making, and he and his colleagues have shown that such simple heuristics frequently lead to better decisions than the theoretically optimal procedure.
From a computational point of view, decision procedures can be encoded in algorithms and heuristics. Edward Tsang argues that the effective rationality of an agent is determined by its computational intelligence. Everything else being equal, an agent that has better algorithms and heuristics could make "more rational" (more optimal) decisions than one that has poorer heuristics and algorithms.
[edit] References
Jon Elster (1983). Sour Grapes: Studies in the Subversion of Rationality. Cambridge, UK: Cambridge University Press.
Gigerenzer, G. & Selten, R. (2002). Bounded Rationality.Cambridge: The MIT Press; reprint edition. ISBN 0-262-57164-1
Hayek, F.A (1948) Individualism and Economic order
Kahneman, Daniel (2003). Maps of bounded rationality: psychology for behavioral economics. The American Economic Review. 93(5). pp. 1449–1475
March, James G. (1994). A Primer on Decision Making: How Decisions Happen. New York: The Free Press.
Rubinstein, A. (1998). Modeling bounded rationality, MIT Press.
Simon, Herbert (1957). "A Behavioral Model of Rational Choice", in Models of Man, Social and Rational: Mathematical Essays on Rational Human Behavior in a Social Setting. New York: Wiley.
Simon, Herbert (1990). A mechanism for social selection and successful altruism, Science 250 (4988): 1665-1668.
Simon, Herbert (1991). Bounded Rationality and Organizational Learning, Organization Science 2(1): 125-134.
Tisdell, Clem (1996). Bounded Rationality and Economic Evolution: A Contribution to Decision Making, Economics, and Management. Cheltenham, UK; Brookfield, Vt.: Edward Elgar.
Tsang, E.P.K. (2008). Computational intelligence determines effective rationality, International Journal on Automation and Control, Vol.5, No.1, 63-66.
Williamson, Oliver (1981). The economies of organization: the transaction cost approach. American Journal of Sociology 87
(3): 548-577.
^ Gigerenzer, Gerd; Selten, Reinhard (2002). Bounded Rationality: The Adaptive Toolbox. MIT Press. ISBN 0262571641. http://books.google.com/books?id=dVMq5UoYS3YC&dq=%22bounded+rationality%22&printsec=frontcover&source=bl&ots=pZ2sZOGH1X&sig=J4icGbo0Wz8MXC3JcX5cHNId298&hl=en&ei=_qziScv-PKa-tAOL9ui0CQ&sa=X&oi=book_result&ct=result&resnum=5#PPA4,M1.
^ "Bounded rationality: Definition from Answers.com". Answers Corporation. http://www.answers.com/topic/bounded-rationality. Retrieved on 2009-04-12.
Some models of human behavior in the social sciences assume that humans can be reasonably approximated or described as "rational" entities (see for example rational choice theory). Many economics models assume that people are on average rational, and can in large enough quantities be approximated to act according to their preferences. The concept of bounded rationality revises this assumption to account for the fact that perfectly rational decisions are often not feasible in practice due to the finite computational resources available for making them.
The term is thought to have been coined by Herbert Simon. In Models of Man, Mr. Simon points out that most people are only partly rational, and are in fact emotional/irrational in the remaining part of their actions. In another work, he states "boundedly rational agents experience limits in formulating and solving complex problems and in processing (receiving, storing, retrieving, transmitting) information" (Williamson, p. 553, citing Simon). Simon describes a number of dimensions along which "classical" models of rationality can be made somewhat more realistic, while sticking within the vein of fairly rigorous formalization. These include:
limiting what sorts of utility functions there might be.
recognizing the costs of gathering and processing information.
the possibility of having a "vector" or "multi-valued" utility function.
Simon suggests that economic agents employ the use of heuristics to make decisions rather than a strict rigid rule of optimization. They do this because of the complexity of the situation, and their inability to process and compute the expected utility of every alternative action. Deliberation costs might be high and there are often other, concurrent economic activities also requiring decisions.
Daniel Kahneman proposes bounded rationality as a model to overcome some of the limitations of the rational-agent models in economic literature.
As decision makers have to make decisions about how and when to decide, Ariel Rubinstein proposed to model bounded rationality by explicitly specifying decision-making procedures. This puts the study of decision procedures on the research agenda.
Gerd Gigerenzer argues that most decision theorists who have discussed bounded rationality have not really followed Simon's ideas about it. Rather, they have either considered how people's decisions might be made sub-optimal by the limitations of human rationality, or have constructed elaborate optimising models of how people might cope with their inability to optimize. Gigerenzer instead proposes to examine simple alternatives to a full rationality analysis as a mechanism for decision making, and he and his colleagues have shown that such simple heuristics frequently lead to better decisions than the theoretically optimal procedure.
From a computational point of view, decision procedures can be encoded in algorithms and heuristics. Edward Tsang argues that the effective rationality of an agent is determined by its computational intelligence. Everything else being equal, an agent that has better algorithms and heuristics could make "more rational" (more optimal) decisions than one that has poorer heuristics and algorithms.
[edit] References
Jon Elster (1983). Sour Grapes: Studies in the Subversion of Rationality. Cambridge, UK: Cambridge University Press.
Gigerenzer, G. & Selten, R. (2002). Bounded Rationality.Cambridge: The MIT Press; reprint edition. ISBN 0-262-57164-1
Hayek, F.A (1948) Individualism and Economic order
Kahneman, Daniel (2003). Maps of bounded rationality: psychology for behavioral economics. The American Economic Review. 93(5). pp. 1449–1475
March, James G. (1994). A Primer on Decision Making: How Decisions Happen. New York: The Free Press.
Rubinstein, A. (1998). Modeling bounded rationality, MIT Press.
Simon, Herbert (1957). "A Behavioral Model of Rational Choice", in Models of Man, Social and Rational: Mathematical Essays on Rational Human Behavior in a Social Setting. New York: Wiley.
Simon, Herbert (1990). A mechanism for social selection and successful altruism, Science 250 (4988): 1665-1668.
Simon, Herbert (1991). Bounded Rationality and Organizational Learning, Organization Science 2(1): 125-134.
Tisdell, Clem (1996). Bounded Rationality and Economic Evolution: A Contribution to Decision Making, Economics, and Management. Cheltenham, UK; Brookfield, Vt.: Edward Elgar.
Tsang, E.P.K. (2008). Computational intelligence determines effective rationality, International Journal on Automation and Control, Vol.5, No.1, 63-66.
Williamson, Oliver (1981). The economies of organization: the transaction cost approach. American Journal of Sociology 87
(3): 548-577.
^ Gigerenzer, Gerd; Selten, Reinhard (2002). Bounded Rationality: The Adaptive Toolbox. MIT Press. ISBN 0262571641. http://books.google.com/books?id=dVMq5UoYS3YC&dq=%22bounded+rationality%22&printsec=frontcover&source=bl&ots=pZ2sZOGH1X&sig=J4icGbo0Wz8MXC3JcX5cHNId298&hl=en&ei=_qziScv-PKa-tAOL9ui0CQ&sa=X&oi=book_result&ct=result&resnum=5#PPA4,M1.
^ "Bounded rationality: Definition from Answers.com". Answers Corporation. http://www.answers.com/topic/bounded-rationality. Retrieved on 2009-04-12.
Human Resource Management
Human resource management (HRM) is the strategic and coherent approach to the management of an organisation's most valued assets - the people working there who individually and collectively contribute to the achievement of the objectives of the business.[1] The terms "human resource management" and "human resources" (HR) have largely replaced the term "personnel management" as a description of the processes involved in managing people in organizations.[1] In simple sense, HRM means employing people, developing their resources, utilizing, maintaining and compensating their services in tune with the job and organizational requirement.
Contents
1 Features
2 Academic theory
2.1 Critical Academic Theory
3 Business practice
4 Careers
5 Professional organizations
6 Functions
7 See also
8 References
[edit] Features
Its features include:
Organizational management
Personnel administration
Manpower management
Industrial management[2][3]
But these traditional expressions are becoming less common for the theoretical discipline. Sometimes even industrial relations and employee relations are confusingly listed as synonyms,[4] although these normally refer to the relationship between management and workers and the behavior of workers in companies.
The theoretical discipline is based primarily on the assumption that employees are individuals with varying goals and needs, and as such should not be thought of as basic business resources, such as trucks and filing cabinets. The field takes a positive view of workers, assuming that virtually all wish to contribute to the enterprise productively, and that the main obstacles to their endeavors are lack of knowledge, insufficient training, and failures of process.
HRM is seen by practitioners in the field as a more innovative view of workplace management than the traditional approach. Its techniques force the managers of an enterprise to express their goals with specificity so that they can be understood and undertaken by the workforce, and to provide the resources needed for them to successfully accomplish their assignments. As such, HRM techniques, when properly practiced, are expressive of the goals and operating practices of the enterprise overall. HRM is also seen by many to have a key role in risk reduction within organizations.[5]
Synonyms such as personnel management are often used in a more restricted sense to describe activities that are necessary in the recruiting of a workforce, providing its members with payroll and benefits, and administrating their work-life needs. So if we move to actual definitions, Torrington and Hall (1987) define personnel management as being:
“a series of activities which: first enable working people and their employing organisations to agree about the objectives and nature of their working relationship and, secondly, ensures that the agreement is fulfilled" (p. 49).
While Miller (1987) suggests that HRM relates to:
".......those decisions and actions which concern the management of employees at all levels in the business and which are related to the implementation of strategies directed towards creating and sustaining competitive advantage" (p. 352).
[edit] Academic theory
The goal of human resource management is to help an organization to meet strategic goals by attracting, and maintaining employees and also to manage them effectively. The key word here perhaps is "fit", i.e. a HRM approach seeks to ensure a fit between the management of an organization's employees, and the overall strategic direction of the company (Miller, 1989).
The basic premise of the academic theory of HRM is that humans are not machines, therefore we need to have an interdisciplinary examination of people in the workplace. Fields such as psychology, industrial engineering, industrial, Legal/Paralegal Studies and organizational psychology, industrial relations, sociology, and critical theories: postmodernism, post-structuralism play a major role. Many colleges and universities offer bachelor and master degrees in Human Resources Management.
One widely used scheme to describe the role of HRM, developed by Dave Ulrich, defines 4 fields for the HRM function:[6]
Strategic business partner
Change management
Employee champion
Administration
However, many HR functions these days struggle to get beyond the roles of administration and employee champion, and are seen rather as reactive than strategically proactive partners for the top management. In addition, HR organizations also have the difficulty in proving how their activities and processes add value to the company. Only in the recent years HR scholars and HR professionals are focusing to develop models that can measure if HR adds value.[7]
[edit] Critical Academic Theory
Postmodernism plays an important part in Academic Theory and particularly in Critical Theory. Indeed Karen Legge in 'Human Resource Management: Rhetorics and Realities' poses the debate of whether HRM is a modernist project or a postmodern discourse (Legge 2004). In many ways, critically or not, many writers contend that HRM itself is an attempt to move away from the modernist traditions of personnel (man as machine) towards a postmodernist view of HRM (man as individuals). Critiques include the notion that because 'Human' is the subject we should recognize that people are complex and that it is only through various discourses that we understand the world. Man is not Machine, no matter what attempts are made to change it i.e. Fordism / Taylorism, McDonaldisation (Modernism).
Critical Theory also questions whether HRM is the pursuit of "attitudinal shaping" (Wilkinson 1998), particularly when considering empowerment, or perhaps more precisely pseudo-empowerment - as the critical perspective notes. Many critics note the move away from Man as Machine is often in many ways, more a Linguistic (discursive) move away than a real attempt to recognise the Human in Human Resource Management.
Critical Theory, in particular postmodernism (poststructualism), recognises that because the subject is people in the workplace, the subject is a complex one, and therefore simplistic notions of 'the best way' or a unitary perspectives on the subject are too simplistic. It also considers the complex subject of power, power games, and office politics. Power in the workplace is a vast and complex subject that cannot be easily defined. This leaves many critics to suggest that Management 'Gurus', consultants, 'best practice' and HR models are often overly simplistic, but in order to sell an idea, they are simplified, and often lead Management as a whole to fall into the trap of oversimplifying the relationship.
[edit] Business practice
Human resources management comprises several processes. Together they are supposed to achieve the above mentioned goal. These processes can be performed in an HR department, but some tasks can also be outsourced or performed by line-managers or other departments. When effectively integrated they provide significant economic benefit to the company.[8]
Workforce planning
Recruitment (sometimes separated into attraction and selection)
Induction and Orientation
Skills management
Training and development
Personnel administration
Compensation in wage or salary
Time management
Travel management (sometimes assigned to accounting rather than HRM)
Payroll (sometimes assigned to accounting rather than HRM)
Employee benefits administration
Personnel cost planning
Performance Appraisal
[edit] Careers
The sort of careers available in HRM are varied. There are generalist HRM jobs such as human resource assistant. There are careers involved with employment, recruitment and placement and these are usually conducted by interviewers, EEO (Equal Employment Opportunity) specialists or college recruiters. Training and development specialism is often conducted by trainers and orientation specialists. Compensation and benefits tasks are handled by compensation analysts, salary administrators, and benefits administrators.
[edit] Professional organizations
Professional organizations in HRM include the Society for Human Resource Management, the Australian Human Resources Institute (AHRI), the Chartered Institute of Personnel and Development (CIPD), the International Public Management Association for HR (IPMA-HR), Management Association of Nepal MAN and the International Personnel Management Association of Canada (IPMA-Canada), Human Capital Institute (HCI)
[edit] Functions
The Human Resources Management (HRM) function includes a variety of activities, and key among them is deciding what staffing needs you have and whether to use independent contractors or hire employees to fill these needs, recruiting and training the best employees, ensuring they are high performers, dealing with performance issues, and ensuring your personnel and management practices conform to various regulations. Activities also include managing your approach to employee benefits and compensation, employee records and personnel policies. Usually small businesses (for-profit or nonprofit) have to carry out these activities themselves because they can't yet afford part- or full-time help. However, they should always ensure that employees have -- and are aware of -- personnel policies which conform to current regulations. These policies are often in the form of employee manuals, which all employees have.
Note that some people distinguish a difference between between HRM (a major management activity) and HRD (Human Resource Development, a profession). Those people might include HRM in HRD, explaining that HRD includes the broader range of activities to develop personnel inside of organizations, including, eg, career development, training, organization development, etc.
There is a long-standing argument about where HR-related functions should be organized into large organizations, eg, "should HR be in the Organization Development department or the other way around?"
The HRM function and HRD profession have undergone tremendous change over the past 20-30 years. Many years ago, large organizations looked to the "Personnel Department," mostly to manage the paperwork around hiring and paying people. More recently, organizations consider the "HR Department" as playing a major role in staffing, training and helping to manage people so that people and the organization are performing at maximum capability in a highly fulfilling manner.
Contents
1 Features
2 Academic theory
2.1 Critical Academic Theory
3 Business practice
4 Careers
5 Professional organizations
6 Functions
7 See also
8 References
[edit] Features
Its features include:
Organizational management
Personnel administration
Manpower management
Industrial management[2][3]
But these traditional expressions are becoming less common for the theoretical discipline. Sometimes even industrial relations and employee relations are confusingly listed as synonyms,[4] although these normally refer to the relationship between management and workers and the behavior of workers in companies.
The theoretical discipline is based primarily on the assumption that employees are individuals with varying goals and needs, and as such should not be thought of as basic business resources, such as trucks and filing cabinets. The field takes a positive view of workers, assuming that virtually all wish to contribute to the enterprise productively, and that the main obstacles to their endeavors are lack of knowledge, insufficient training, and failures of process.
HRM is seen by practitioners in the field as a more innovative view of workplace management than the traditional approach. Its techniques force the managers of an enterprise to express their goals with specificity so that they can be understood and undertaken by the workforce, and to provide the resources needed for them to successfully accomplish their assignments. As such, HRM techniques, when properly practiced, are expressive of the goals and operating practices of the enterprise overall. HRM is also seen by many to have a key role in risk reduction within organizations.[5]
Synonyms such as personnel management are often used in a more restricted sense to describe activities that are necessary in the recruiting of a workforce, providing its members with payroll and benefits, and administrating their work-life needs. So if we move to actual definitions, Torrington and Hall (1987) define personnel management as being:
“a series of activities which: first enable working people and their employing organisations to agree about the objectives and nature of their working relationship and, secondly, ensures that the agreement is fulfilled" (p. 49).
While Miller (1987) suggests that HRM relates to:
".......those decisions and actions which concern the management of employees at all levels in the business and which are related to the implementation of strategies directed towards creating and sustaining competitive advantage" (p. 352).
[edit] Academic theory
The goal of human resource management is to help an organization to meet strategic goals by attracting, and maintaining employees and also to manage them effectively. The key word here perhaps is "fit", i.e. a HRM approach seeks to ensure a fit between the management of an organization's employees, and the overall strategic direction of the company (Miller, 1989).
The basic premise of the academic theory of HRM is that humans are not machines, therefore we need to have an interdisciplinary examination of people in the workplace. Fields such as psychology, industrial engineering, industrial, Legal/Paralegal Studies and organizational psychology, industrial relations, sociology, and critical theories: postmodernism, post-structuralism play a major role. Many colleges and universities offer bachelor and master degrees in Human Resources Management.
One widely used scheme to describe the role of HRM, developed by Dave Ulrich, defines 4 fields for the HRM function:[6]
Strategic business partner
Change management
Employee champion
Administration
However, many HR functions these days struggle to get beyond the roles of administration and employee champion, and are seen rather as reactive than strategically proactive partners for the top management. In addition, HR organizations also have the difficulty in proving how their activities and processes add value to the company. Only in the recent years HR scholars and HR professionals are focusing to develop models that can measure if HR adds value.[7]
[edit] Critical Academic Theory
Postmodernism plays an important part in Academic Theory and particularly in Critical Theory. Indeed Karen Legge in 'Human Resource Management: Rhetorics and Realities' poses the debate of whether HRM is a modernist project or a postmodern discourse (Legge 2004). In many ways, critically or not, many writers contend that HRM itself is an attempt to move away from the modernist traditions of personnel (man as machine) towards a postmodernist view of HRM (man as individuals). Critiques include the notion that because 'Human' is the subject we should recognize that people are complex and that it is only through various discourses that we understand the world. Man is not Machine, no matter what attempts are made to change it i.e. Fordism / Taylorism, McDonaldisation (Modernism).
Critical Theory also questions whether HRM is the pursuit of "attitudinal shaping" (Wilkinson 1998), particularly when considering empowerment, or perhaps more precisely pseudo-empowerment - as the critical perspective notes. Many critics note the move away from Man as Machine is often in many ways, more a Linguistic (discursive) move away than a real attempt to recognise the Human in Human Resource Management.
Critical Theory, in particular postmodernism (poststructualism), recognises that because the subject is people in the workplace, the subject is a complex one, and therefore simplistic notions of 'the best way' or a unitary perspectives on the subject are too simplistic. It also considers the complex subject of power, power games, and office politics. Power in the workplace is a vast and complex subject that cannot be easily defined. This leaves many critics to suggest that Management 'Gurus', consultants, 'best practice' and HR models are often overly simplistic, but in order to sell an idea, they are simplified, and often lead Management as a whole to fall into the trap of oversimplifying the relationship.
[edit] Business practice
Human resources management comprises several processes. Together they are supposed to achieve the above mentioned goal. These processes can be performed in an HR department, but some tasks can also be outsourced or performed by line-managers or other departments. When effectively integrated they provide significant economic benefit to the company.[8]
Workforce planning
Recruitment (sometimes separated into attraction and selection)
Induction and Orientation
Skills management
Training and development
Personnel administration
Compensation in wage or salary
Time management
Travel management (sometimes assigned to accounting rather than HRM)
Payroll (sometimes assigned to accounting rather than HRM)
Employee benefits administration
Personnel cost planning
Performance Appraisal
[edit] Careers
The sort of careers available in HRM are varied. There are generalist HRM jobs such as human resource assistant. There are careers involved with employment, recruitment and placement and these are usually conducted by interviewers, EEO (Equal Employment Opportunity) specialists or college recruiters. Training and development specialism is often conducted by trainers and orientation specialists. Compensation and benefits tasks are handled by compensation analysts, salary administrators, and benefits administrators.
[edit] Professional organizations
Professional organizations in HRM include the Society for Human Resource Management, the Australian Human Resources Institute (AHRI), the Chartered Institute of Personnel and Development (CIPD), the International Public Management Association for HR (IPMA-HR), Management Association of Nepal MAN and the International Personnel Management Association of Canada (IPMA-Canada), Human Capital Institute (HCI)
[edit] Functions
The Human Resources Management (HRM) function includes a variety of activities, and key among them is deciding what staffing needs you have and whether to use independent contractors or hire employees to fill these needs, recruiting and training the best employees, ensuring they are high performers, dealing with performance issues, and ensuring your personnel and management practices conform to various regulations. Activities also include managing your approach to employee benefits and compensation, employee records and personnel policies. Usually small businesses (for-profit or nonprofit) have to carry out these activities themselves because they can't yet afford part- or full-time help. However, they should always ensure that employees have -- and are aware of -- personnel policies which conform to current regulations. These policies are often in the form of employee manuals, which all employees have.
Note that some people distinguish a difference between between HRM (a major management activity) and HRD (Human Resource Development, a profession). Those people might include HRM in HRD, explaining that HRD includes the broader range of activities to develop personnel inside of organizations, including, eg, career development, training, organization development, etc.
There is a long-standing argument about where HR-related functions should be organized into large organizations, eg, "should HR be in the Organization Development department or the other way around?"
The HRM function and HRD profession have undergone tremendous change over the past 20-30 years. Many years ago, large organizations looked to the "Personnel Department," mostly to manage the paperwork around hiring and paying people. More recently, organizations consider the "HR Department" as playing a major role in staffing, training and helping to manage people so that people and the organization are performing at maximum capability in a highly fulfilling manner.
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