Scenario-based projections of social processes

As we have noted in previous posts, social outcomes are highly path-dependent and contingent (link, link, link, link). This implies that it is difficult to predict the consequences of even a single causal intervention within a complex social environment including numerous actors -- say, a new land use policy, a new state tax on services, or a sweeping cap-and-trade policy on CO2 emissions. And yet policy changes are specifically designed and chosen in order to bring about certain kinds of outcomes. We care about the future; we adopt policies to improve this or that feature of the future; and yet we have a hard time providing a justified forecast of the consequences of the policy.

This difficulty doesn't only affect policy choices; it also pertains to large interventions like the democracy uprisings in the Middle East and North Africa. There are too many imponderable factors -- the behavior of the military, the reactions of other governments, the consequent strategies of internal political actors and parties (the Muslim Brotherhood in Egypt) -- so activists and academic experts alike are forced to concede that they don't really know what the consequences will be.

One part of this imponderability derives from the fact that social changes are conveyed through sets of individual and collective actors. The actors have a variety of motives and modes of reasoning, and the collective actors are forced to somehow aggregate the actions and wants of subordinate actors. And it isn't possible to anticipate with confidence the choices that the actors will make in response to changing circumstances. At a very high level of abstraction, it is the task of game theory to model strategic decision-making over a sequence of choices (problems of strategic rationality); but the tools of game theory are too abstract to allow modeling of specific complex social interactions.

A second feature of unpredictability in extended social processes derives from the fact that the agents themselves are not fixed and constant throughout the process. The experience of democracy activism potentially changes the agent profoundly -- so the expectations we would have had of his/her choices at the beginning may be very poorly grounded by the middle and end. Some possible changes may make a very large difference in outcomes -- actors may become more committed, more open to violence, more ready to compromise, more understanding of the grievances of other groups, ... This is sometimes described as endogeneity -- the causal components themselves change their characteristics as a consequence of the process.

So the actors change through the social process; but the same is often true of the social organizations and institutions that are involved in the process. Take contentious politics -- it may be that a round of protests begins around a couple of loose pre-existing organizations. As actors seek to achieve their political goals through collective action, they make use of the organizations for their communications and mobilization resources. But some actors may then also attempt to transform the organization itself -- to make it more effective or to make it more accommodating to the political objectives of this particular group of activists. (Think of Lenin as a revolutionary organization innovator.) And through their struggles, they may elicit changes in the organizations of the "forces of order" -- the police may create new tactics (kettling) and new sub-organizations (specialized intelligence units). So the process of change is likely enough to transform all the causal components as well -- the agents and their motivations as well as the surrounding institutions of mobilization and control. Rather than a set of billiard balls and iron rods with fixed properties and predictable aggregate consequences, we find a fluid situation in which the causal properties of each of the components of the process are themselves changing.

One way of trying to handle the indeterminacy and causal complexity of these sorts of causal processes is to give up on the goal of arriving at specific "point" predictions about outcomes and instead concentrate on tracing out a large number of possible scenarios, beginning with the circumstances, actors, and structures on the ground. In some circumstances we may find that there is a very wide range of possible outcomes; but we may find that a large percentage of the feasible scenarios or pathways fall within a much narrower range. This kind of reasoning is familiar to economists and financial analysts in the form of Monte Carlo simulations. And it is possible that the approach can be used for modeling likely outcomes in more complex social processes as well -- war and peace, ethnic conflict, climate change, or democracy movements.

Agent-based modeling is one component of approaches like these (link).  This means taking into account a wide range of social factors -- agents, groups, organizations, institutions, states, popular movements, and then modeling the consequences of these initial assumptions. Robert Axelrod and colleagues have applied a variety of modeling techniques to these efforts (link).

Another interesting effort to carry out such an effort is underway at the RAND Pardee Center, summarized in a white paper called Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. Here is how the lead investigators describe the overall strategy of the effort:

This report describes and demonstrates a new, quantitative approach to long-term policy analysis (LTPA).  These robust decisionmaking methods aim to greatly enhance and support humans’ innate decisionmaking capabilities with powerful quantitative analytic tools similar to those that have demonstrated unparalleled effectiveness when applied to more circumscribed decision problems.  By reframing the question “What will the long-term future bring?” as “How can we choose actions today that will be consistent with our long-term interests?” robust decisionmaking can harness the heretofore unavailable capabilities of modern computers to grapple directly with the inherent difficulty of accurate long-term prediction that has bedeviled previous approaches to LTPA. (iii)

LTPA is an important example of a class of problems requiring decisionmaking under conditions of  deep uncertainty—that is, where analysts do not know, or the parties to a decision cannot agree on, (1) the appropriate conceptual models that describe the relationships among the key driving forces that will shape the long-term future, (2) the probability distributions used to represent uncertainty about key variables and parameters in the mathematical representations of these conceptual models, and/or (3) how to value the desirability of alternative outcomes. (iii)

And here, in a nutshell, is how the approach is supposed to work:

This study proposes four key elements of successful LTPA: 

• Consider large ensembles (hundreds to millions) of scenarios.
• Seek robust, not optimal, strategies.
• Achieve robustness with adaptivity.
• Design analysis for interactive exploration of the multiplicity of plausible futures. 

These elements are implemented through an iterative process in which the computer helps humans create a large ensemble of plausible scenarios, where each scenario represents one guess about how the world works (a future state of the world) and one choice of many alternative strategies that might be adopted to influence outcomes. Ideally, such ensembles will contain a sufficiently wide range of plausible futures that one will match whatever future, surprising or not, does occur—at least close enough for the purposes of crafting policies robust against it.  (xiii)

Thus, computer-guided exploration of scenario and decision spaces can provide a prosthesis for the imagination, helping humans, working individually or in groups, to discover adaptive near-term strategies that are robust over large ensembles of plausible futures. (xiv)

The hard work of this approach is to identify the characteristics of policy levers, exogenous uncertainties, measures, and relationship (XLRM).  Then the analysis turns to identifying a very large number of possible scenarios, depending on the initial conditions and the properties of the actors and organizations. (This aspect of the analysis is analogous to multiple plays of a simulation game like SimCity.) Finally, the approach requires aggregating the large number of scenarios to allow the analysis to reach some conclusions about the distribution of futures entailed by the starting position and the characteristics of the actors and institutions.  And the method attempts to assign a measure of "regret" to outcomes, in order to assess the policy steps that might be taken today that lead to the least regrettable outcomes in the distant future.

It appears, then, that there are computational tools and methods that may prove useful for social explanation and social prediction -- not of single outcomes, but of the range of outcomes that may be associated with a set of interventions, actors, and institutions.

Scenario-based projections of social processes

As we have noted in previous posts, social outcomes are highly path-dependent and contingent (link, link, link, link). This implies that it is difficult to predict the consequences of even a single causal intervention within a complex social environment including numerous actors -- say, a new land use policy, a new state tax on services, or a sweeping cap-and-trade policy on CO2 emissions. And yet policy changes are specifically designed and chosen in order to bring about certain kinds of outcomes. We care about the future; we adopt policies to improve this or that feature of the future; and yet we have a hard time providing a justified forecast of the consequences of the policy.

This difficulty doesn't only affect policy choices; it also pertains to large interventions like the democracy uprisings in the Middle East and North Africa. There are too many imponderable factors -- the behavior of the military, the reactions of other governments, the consequent strategies of internal political actors and parties (the Muslim Brotherhood in Egypt) -- so activists and academic experts alike are forced to concede that they don't really know what the consequences will be.

One part of this imponderability derives from the fact that social changes are conveyed through sets of individual and collective actors. The actors have a variety of motives and modes of reasoning, and the collective actors are forced to somehow aggregate the actions and wants of subordinate actors. And it isn't possible to anticipate with confidence the choices that the actors will make in response to changing circumstances. At a very high level of abstraction, it is the task of game theory to model strategic decision-making over a sequence of choices (problems of strategic rationality); but the tools of game theory are too abstract to allow modeling of specific complex social interactions.

A second feature of unpredictability in extended social processes derives from the fact that the agents themselves are not fixed and constant throughout the process. The experience of democracy activism potentially changes the agent profoundly -- so the expectations we would have had of his/her choices at the beginning may be very poorly grounded by the middle and end. Some possible changes may make a very large difference in outcomes -- actors may become more committed, more open to violence, more ready to compromise, more understanding of the grievances of other groups, ... This is sometimes described as endogeneity -- the causal components themselves change their characteristics as a consequence of the process.

So the actors change through the social process; but the same is often true of the social organizations and institutions that are involved in the process. Take contentious politics -- it may be that a round of protests begins around a couple of loose pre-existing organizations. As actors seek to achieve their political goals through collective action, they make use of the organizations for their communications and mobilization resources. But some actors may then also attempt to transform the organization itself -- to make it more effective or to make it more accommodating to the political objectives of this particular group of activists. (Think of Lenin as a revolutionary organization innovator.) And through their struggles, they may elicit changes in the organizations of the "forces of order" -- the police may create new tactics (kettling) and new sub-organizations (specialized intelligence units). So the process of change is likely enough to transform all the causal components as well -- the agents and their motivations as well as the surrounding institutions of mobilization and control. Rather than a set of billiard balls and iron rods with fixed properties and predictable aggregate consequences, we find a fluid situation in which the causal properties of each of the components of the process are themselves changing.

One way of trying to handle the indeterminacy and causal complexity of these sorts of causal processes is to give up on the goal of arriving at specific "point" predictions about outcomes and instead concentrate on tracing out a large number of possible scenarios, beginning with the circumstances, actors, and structures on the ground. In some circumstances we may find that there is a very wide range of possible outcomes; but we may find that a large percentage of the feasible scenarios or pathways fall within a much narrower range. This kind of reasoning is familiar to economists and financial analysts in the form of Monte Carlo simulations. And it is possible that the approach can be used for modeling likely outcomes in more complex social processes as well -- war and peace, ethnic conflict, climate change, or democracy movements.

Agent-based modeling is one component of approaches like these (link).  This means taking into account a wide range of social factors -- agents, groups, organizations, institutions, states, popular movements, and then modeling the consequences of these initial assumptions. Robert Axelrod and colleagues have applied a variety of modeling techniques to these efforts (link).

Another interesting effort to carry out such an effort is underway at the RAND Pardee Center, summarized in a white paper called Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. Here is how the lead investigators describe the overall strategy of the effort:

This report describes and demonstrates a new, quantitative approach to long-term policy analysis (LTPA).  These robust decisionmaking methods aim to greatly enhance and support humans’ innate decisionmaking capabilities with powerful quantitative analytic tools similar to those that have demonstrated unparalleled effectiveness when applied to more circumscribed decision problems.  By reframing the question “What will the long-term future bring?” as “How can we choose actions today that will be consistent with our long-term interests?” robust decisionmaking can harness the heretofore unavailable capabilities of modern computers to grapple directly with the inherent difficulty of accurate long-term prediction that has bedeviled previous approaches to LTPA. (iii)

LTPA is an important example of a class of problems requiring decisionmaking under conditions of  deep uncertainty—that is, where analysts do not know, or the parties to a decision cannot agree on, (1) the appropriate conceptual models that describe the relationships among the key driving forces that will shape the long-term future, (2) the probability distributions used to represent uncertainty about key variables and parameters in the mathematical representations of these conceptual models, and/or (3) how to value the desirability of alternative outcomes. (iii)

And here, in a nutshell, is how the approach is supposed to work:

This study proposes four key elements of successful LTPA: 

• Consider large ensembles (hundreds to millions) of scenarios.
• Seek robust, not optimal, strategies.
• Achieve robustness with adaptivity.
• Design analysis for interactive exploration of the multiplicity of plausible futures. 

These elements are implemented through an iterative process in which the computer helps humans create a large ensemble of plausible scenarios, where each scenario represents one guess about how the world works (a future state of the world) and one choice of many alternative strategies that might be adopted to influence outcomes. Ideally, such ensembles will contain a sufficiently wide range of plausible futures that one will match whatever future, surprising or not, does occur—at least close enough for the purposes of crafting policies robust against it.  (xiii)

Thus, computer-guided exploration of scenario and decision spaces can provide a prosthesis for the imagination, helping humans, working individually or in groups, to discover adaptive near-term strategies that are robust over large ensembles of plausible futures. (xiv)

The hard work of this approach is to identify the characteristics of policy levers, exogenous uncertainties, measures, and relationship (XLRM).  Then the analysis turns to identifying a very large number of possible scenarios, depending on the initial conditions and the properties of the actors and organizations. (This aspect of the analysis is analogous to multiple plays of a simulation game like SimCity.) Finally, the approach requires aggregating the large number of scenarios to allow the analysis to reach some conclusions about the distribution of futures entailed by the starting position and the characteristics of the actors and institutions.  And the method attempts to assign a measure of "regret" to outcomes, in order to assess the policy steps that might be taken today that lead to the least regrettable outcomes in the distant future.

It appears, then, that there are computational tools and methods that may prove useful for social explanation and social prediction -- not of single outcomes, but of the range of outcomes that may be associated with a set of interventions, actors, and institutions.

Scenario-based projections of social processes

As we have noted in previous posts, social outcomes are highly path-dependent and contingent (link, link, link, link). This implies that it is difficult to predict the consequences of even a single causal intervention within a complex social environment including numerous actors -- say, a new land use policy, a new state tax on services, or a sweeping cap-and-trade policy on CO2 emissions. And yet policy changes are specifically designed and chosen in order to bring about certain kinds of outcomes. We care about the future; we adopt policies to improve this or that feature of the future; and yet we have a hard time providing a justified forecast of the consequences of the policy.

This difficulty doesn't only affect policy choices; it also pertains to large interventions like the democracy uprisings in the Middle East and North Africa. There are too many imponderable factors -- the behavior of the military, the reactions of other governments, the consequent strategies of internal political actors and parties (the Muslim Brotherhood in Egypt) -- so activists and academic experts alike are forced to concede that they don't really know what the consequences will be.

One part of this imponderability derives from the fact that social changes are conveyed through sets of individual and collective actors. The actors have a variety of motives and modes of reasoning, and the collective actors are forced to somehow aggregate the actions and wants of subordinate actors. And it isn't possible to anticipate with confidence the choices that the actors will make in response to changing circumstances. At a very high level of abstraction, it is the task of game theory to model strategic decision-making over a sequence of choices (problems of strategic rationality); but the tools of game theory are too abstract to allow modeling of specific complex social interactions.

A second feature of unpredictability in extended social processes derives from the fact that the agents themselves are not fixed and constant throughout the process. The experience of democracy activism potentially changes the agent profoundly -- so the expectations we would have had of his/her choices at the beginning may be very poorly grounded by the middle and end. Some possible changes may make a very large difference in outcomes -- actors may become more committed, more open to violence, more ready to compromise, more understanding of the grievances of other groups, ... This is sometimes described as endogeneity -- the causal components themselves change their characteristics as a consequence of the process.

So the actors change through the social process; but the same is often true of the social organizations and institutions that are involved in the process. Take contentious politics -- it may be that a round of protests begins around a couple of loose pre-existing organizations. As actors seek to achieve their political goals through collective action, they make use of the organizations for their communications and mobilization resources. But some actors may then also attempt to transform the organization itself -- to make it more effective or to make it more accommodating to the political objectives of this particular group of activists. (Think of Lenin as a revolutionary organization innovator.) And through their struggles, they may elicit changes in the organizations of the "forces of order" -- the police may create new tactics (kettling) and new sub-organizations (specialized intelligence units). So the process of change is likely enough to transform all the causal components as well -- the agents and their motivations as well as the surrounding institutions of mobilization and control. Rather than a set of billiard balls and iron rods with fixed properties and predictable aggregate consequences, we find a fluid situation in which the causal properties of each of the components of the process are themselves changing.

One way of trying to handle the indeterminacy and causal complexity of these sorts of causal processes is to give up on the goal of arriving at specific "point" predictions about outcomes and instead concentrate on tracing out a large number of possible scenarios, beginning with the circumstances, actors, and structures on the ground. In some circumstances we may find that there is a very wide range of possible outcomes; but we may find that a large percentage of the feasible scenarios or pathways fall within a much narrower range. This kind of reasoning is familiar to economists and financial analysts in the form of Monte Carlo simulations. And it is possible that the approach can be used for modeling likely outcomes in more complex social processes as well -- war and peace, ethnic conflict, climate change, or democracy movements.

Agent-based modeling is one component of approaches like these (link).  This means taking into account a wide range of social factors -- agents, groups, organizations, institutions, states, popular movements, and then modeling the consequences of these initial assumptions. Robert Axelrod and colleagues have applied a variety of modeling techniques to these efforts (link).

Another interesting effort to carry out such an effort is underway at the RAND Pardee Center, summarized in a white paper called Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. Here is how the lead investigators describe the overall strategy of the effort:

This report describes and demonstrates a new, quantitative approach to long-term policy analysis (LTPA).  These robust decisionmaking methods aim to greatly enhance and support humans’ innate decisionmaking capabilities with powerful quantitative analytic tools similar to those that have demonstrated unparalleled effectiveness when applied to more circumscribed decision problems.  By reframing the question “What will the long-term future bring?” as “How can we choose actions today that will be consistent with our long-term interests?” robust decisionmaking can harness the heretofore unavailable capabilities of modern computers to grapple directly with the inherent difficulty of accurate long-term prediction that has bedeviled previous approaches to LTPA. (iii)

LTPA is an important example of a class of problems requiring decisionmaking under conditions of  deep uncertainty—that is, where analysts do not know, or the parties to a decision cannot agree on, (1) the appropriate conceptual models that describe the relationships among the key driving forces that will shape the long-term future, (2) the probability distributions used to represent uncertainty about key variables and parameters in the mathematical representations of these conceptual models, and/or (3) how to value the desirability of alternative outcomes. (iii)

And here, in a nutshell, is how the approach is supposed to work:

This study proposes four key elements of successful LTPA: 

• Consider large ensembles (hundreds to millions) of scenarios.
• Seek robust, not optimal, strategies.
• Achieve robustness with adaptivity.
• Design analysis for interactive exploration of the multiplicity of plausible futures. 

These elements are implemented through an iterative process in which the computer helps humans create a large ensemble of plausible scenarios, where each scenario represents one guess about how the world works (a future state of the world) and one choice of many alternative strategies that might be adopted to influence outcomes. Ideally, such ensembles will contain a sufficiently wide range of plausible futures that one will match whatever future, surprising or not, does occur—at least close enough for the purposes of crafting policies robust against it.  (xiii)

Thus, computer-guided exploration of scenario and decision spaces can provide a prosthesis for the imagination, helping humans, working individually or in groups, to discover adaptive near-term strategies that are robust over large ensembles of plausible futures. (xiv)

The hard work of this approach is to identify the characteristics of policy levers, exogenous uncertainties, measures, and relationship (XLRM).  Then the analysis turns to identifying a very large number of possible scenarios, depending on the initial conditions and the properties of the actors and organizations. (This aspect of the analysis is analogous to multiple plays of a simulation game like SimCity.) Finally, the approach requires aggregating the large number of scenarios to allow the analysis to reach some conclusions about the distribution of futures entailed by the starting position and the characteristics of the actors and institutions.  And the method attempts to assign a measure of "regret" to outcomes, in order to assess the policy steps that might be taken today that lead to the least regrettable outcomes in the distant future.

It appears, then, that there are computational tools and methods that may prove useful for social explanation and social prediction -- not of single outcomes, but of the range of outcomes that may be associated with a set of interventions, actors, and institutions.

The inexact science of economics

Image: social accounting matrix, Bolivia, 1997

Economics is an "inexact" science; or so Daniel Hausman argues in The Inexact and Separate Science of Economics (Google Books link).  As it implies, this description conveys that economic laws have only a loose fit with observed economic behavior.  Here are the loosely related interpretations that Hausman offers for this idea, drawing on the thinking of John Stuart Mill:

1. Inexact laws are approximate.  They are true within some margin of error.
2. Inexact laws are probabilistic or statistical.  Instead of stating how human beings always behave, economic laws state how they usually behave.
3. Inexact laws make counterfactual assertions about how things would be in the absence of interferences.
4. Inexact laws are qualified with vague ceteris paribus clauses. (128)

Economics has also been treated by economists as a separate science: a science capable of explaining virtually all the phenomena in a reasonably well-defined domain of social phenomena.  Here is Hausman's interpretation of a separate science:

1. Economics is defined in terms of the causal factors with which it is concerned, not in terms of a domain.
2. Economics has a distinct domain, in which its causal factors predominate.
3. The "laws" of the predominating causal factors are already reasonably well-known.
4. Thus, economic theory, which employs these laws, provides a unified, complete, but inexact account of its domain. (90-91)

These characteristics of economic theories and models have implications for several important areas: truth, prediction, explanation, and confirmation.  Is economics a scientific theory of existing observable economic phenomena?  Or is it an abstract, hypothetical model with only tangential implications for the observable social world?  Is economics an empirical science or a mathematical system?

Let's look at these questions in turn.  First, can we give a good interpretation of what it would mean to believe that an inexact theory or law is "true"?  Here is a possible answer: we may believe that there are real but unobservable causal processes that "drive" social phenomena.  To say that a social or economic theory is true is to say that it correctly identifies a real causal process -- whether or not that process operates with sufficient separation to give rise to strict empirical consequences.  Galilean laws of mechanics are true for falling objects, even if feathers follow unpredictable trajectories through turbulent gases.

Second, how can we reconcile the desire to use economic theories to make predictions about future states with the acknowledged inexactness of those theories and laws? If a theory includes hypotheses about underlying causal mechanisms that are true in the sense just mentioned, then a certain kind of prediction is justified as well: "in the absence of confounding causal factors, the presence of X will give rise to Y." But of course this is a useless predictive statement in the current situation, since the whole point is that economic processes rarely or never operate in isolation. So we are more or less compelled to conclude that theories based on inexact laws are not a useable ground for empirical prediction.

Third, in what sense do the deductive consequences of an inexact theory "explain" a given outcome -- either one that is consistent with those consequences or one that is inconsistent with the consequences? Here inexact laws are on stronger ground: after the fact, it is often possible to demonstrate that the mechanisms that led to an outcome are those specified by the theory. Explanation and prediction are not equIvalent. Natural selection explains the features of Darwin's finches -- but it doesn't permit prediction of future evolutionary change.

And finally, what is involved in trying to use empirical data to confirm or disconfirm an inexact theory?  Given that we have stipulated that the theory has false consequences, we can't use standard confirmation theory.  So what kind of empirical argument would help provide empirical evaluation of an inexact theory?  One possibility is that we might require that the predictions of the theory should fall within a certain range of the observable measurements -- which is implied by the idea of "approximately true" consequences.  But actually, it is possible that we might hold that a given theory is inexact, true, and wildly divergent from observed experience.  (This would be true of the application of classical mechanics to the problem of describing the behavior of light, irregular objects shot out of guns under water.)  Hausman confronts this type of issue when he asks why we should believe that the premises of general equilibrium theory are true. But here too there are alternatives, including piecemeal confirmation of individual causal hypotheses. Hausman refers to this possibility as a version of Mill's deductive method.

I take up some of these questions in my article, "Economic Models in Development Economics" link, included in On the Reliability of Economic Models: Essays in the Philosophy of Economics.  This article discusses some related questions about the reliability and applicability of computable general equilibrium models in application to the observed behavior of real economies.  Here are some concluding thoughts from that article concerning the empirical and logical features that are relevant to the assessment of CGE models:

"The general problem of the antecedent credibility of an economic model can be broken down into more specific questions concerning the validity, comprehensiveness, robustness, reliability, and autonomy of the model. I will define these concepts in the following terms.

• Validity is a measure of the degree to which the assumptions employed in the construction of the model are thought to correspond to the real processes underlying the phenomena represented by the model.
• Comprehensiveness is the degree to which the model is thought to succeed in capturing the major causal factors that influence the features of the behavior of the system in which we are interested.
• Robustness is a measure of the degree to which the results of the model persist under small perturbations in the settings of parameters, formulation of equations, etc.
• Autonomy refers to the stability of the model's results in face of variation of contextual factors.
• Reliability is a measure of the degree of confidence we can have in the data employed in setting the values of the parameters.

These are epistemic features of models that can be investigated more or less independently and prior to examination of the empirical success or failure of the predictions of the model."

(Hausman's book is virtually definitive in its formulation of the tasks and scope of the philosophy of economics.  When conjoined with the book he wrote with Michael McPherson, Economic Analysis, Moral Philosophy and Public Policy, the philosophy of economics itself becomes a "separate science": virtually all the important questions are raised throughout a bounded domain, and a reasonable set of theories are offered to answer those questions.)

The inexact science of economics

Image: social accounting matrix, Bolivia, 1997

Economics is an "inexact" science; or so Daniel Hausman argues in The Inexact and Separate Science of Economics (Google Books link).  As it implies, this description conveys that economic laws have only a loose fit with observed economic behavior.  Here are the loosely related interpretations that Hausman offers for this idea, drawing on the thinking of John Stuart Mill:

1. Inexact laws are approximate.  They are true within some margin of error.
2. Inexact laws are probabilistic or statistical.  Instead of stating how human beings always behave, economic laws state how they usually behave.
3. Inexact laws make counterfactual assertions about how things would be in the absence of interferences.
4. Inexact laws are qualified with vague ceteris paribus clauses. (128)

Economics has also been treated by economists as a separate science: a science capable of explaining virtually all the phenomena in a reasonably well-defined domain of social phenomena.  Here is Hausman's interpretation of a separate science:

1. Economics is defined in terms of the causal factors with which it is concerned, not in terms of a domain.
2. Economics has a distinct domain, in which its causal factors predominate.
3. The "laws" of the predominating causal factors are already reasonably well-known.
4. Thus, economic theory, which employs these laws, provides a unified, complete, but inexact account of its domain. (90-91)

These characteristics of economic theories and models have implications for several important areas: truth, prediction, explanation, and confirmation.  Is economics a scientific theory of existing observable economic phenomena?  Or is it an abstract, hypothetical model with only tangential implications for the observable social world?  Is economics an empirical science or a mathematical system?

Let's look at these questions in turn.  First, can we give a good interpretation of what it would mean to believe that an inexact theory or law is "true"?  Here is a possible answer: we may believe that there are real but unobservable causal processes that "drive" social phenomena.  To say that a social or economic theory is true is to say that it correctly identifies a real causal process -- whether or not that process operates with sufficient separation to give rise to strict empirical consequences.  Galilean laws of mechanics are true for falling objects, even if feathers follow unpredictable trajectories through turbulent gases.

Second, how can we reconcile the desire to use economic theories to make predictions about future states with the acknowledged inexactness of those theories and laws? If a theory includes hypotheses about underlying causal mechanisms that are true in the sense just mentioned, then a certain kind of prediction is justified as well: "in the absence of confounding causal factors, the presence of X will give rise to Y." But of course this is a useless predictive statement in the current situation, since the whole point is that economic processes rarely or never operate in isolation. So we are more or less compelled to conclude that theories based on inexact laws are not a useable ground for empirical prediction.

Third, in what sense do the deductive consequences of an inexact theory "explain" a given outcome -- either one that is consistent with those consequences or one that is inconsistent with the consequences? Here inexact laws are on stronger ground: after the fact, it is often possible to demonstrate that the mechanisms that led to an outcome are those specified by the theory. Explanation and prediction are not equIvalent. Natural selection explains the features of Darwin's finches -- but it doesn't permit prediction of future evolutionary change.

And finally, what is involved in trying to use empirical data to confirm or disconfirm an inexact theory?  Given that we have stipulated that the theory has false consequences, we can't use standard confirmation theory.  So what kind of empirical argument would help provide empirical evaluation of an inexact theory?  One possibility is that we might require that the predictions of the theory should fall within a certain range of the observable measurements -- which is implied by the idea of "approximately true" consequences.  But actually, it is possible that we might hold that a given theory is inexact, true, and wildly divergent from observed experience.  (This would be true of the application of classical mechanics to the problem of describing the behavior of light, irregular objects shot out of guns under water.)  Hausman confronts this type of issue when he asks why we should believe that the premises of general equilibrium theory are true. But here too there are alternatives, including piecemeal confirmation of individual causal hypotheses. Hausman refers to this possibility as a version of Mill's deductive method.

I take up some of these questions in my article, "Economic Models in Development Economics" link, included in On the Reliability of Economic Models: Essays in the Philosophy of Economics.  This article discusses some related questions about the reliability and applicability of computable general equilibrium models in application to the observed behavior of real economies.  Here are some concluding thoughts from that article concerning the empirical and logical features that are relevant to the assessment of CGE models:

"The general problem of the antecedent credibility of an economic model can be broken down into more specific questions concerning the validity, comprehensiveness, robustness, reliability, and autonomy of the model. I will define these concepts in the following terms.

• Validity is a measure of the degree to which the assumptions employed in the construction of the model are thought to correspond to the real processes underlying the phenomena represented by the model.
• Comprehensiveness is the degree to which the model is thought to succeed in capturing the major causal factors that influence the features of the behavior of the system in which we are interested.
• Robustness is a measure of the degree to which the results of the model persist under small perturbations in the settings of parameters, formulation of equations, etc.
• Autonomy refers to the stability of the model's results in face of variation of contextual factors.
• Reliability is a measure of the degree of confidence we can have in the data employed in setting the values of the parameters.

These are epistemic features of models that can be investigated more or less independently and prior to examination of the empirical success or failure of the predictions of the model."

(Hausman's book is virtually definitive in its formulation of the tasks and scope of the philosophy of economics.  When conjoined with the book he wrote with Michael McPherson, Economic Analysis, Moral Philosophy and Public Policy, the philosophy of economics itself becomes a "separate science": virtually all the important questions are raised throughout a bounded domain, and a reasonable set of theories are offered to answer those questions.)

The inexact science of economics

Image: social accounting matrix, Bolivia, 1997

Economics is an "inexact" science; or so Daniel Hausman argues in The Inexact and Separate Science of Economics (Google Books link).  As it implies, this description conveys that economic laws have only a loose fit with observed economic behavior.  Here are the loosely related interpretations that Hausman offers for this idea, drawing on the thinking of John Stuart Mill:

1. Inexact laws are approximate.  They are true within some margin of error.
2. Inexact laws are probabilistic or statistical.  Instead of stating how human beings always behave, economic laws state how they usually behave.
3. Inexact laws make counterfactual assertions about how things would be in the absence of interferences.
4. Inexact laws are qualified with vague ceteris paribus clauses. (128)

Economics has also been treated by economists as a separate science: a science capable of explaining virtually all the phenomena in a reasonably well-defined domain of social phenomena.  Here is Hausman's interpretation of a separate science:

1. Economics is defined in terms of the causal factors with which it is concerned, not in terms of a domain.
2. Economics has a distinct domain, in which its causal factors predominate.
3. The "laws" of the predominating causal factors are already reasonably well-known.
4. Thus, economic theory, which employs these laws, provides a unified, complete, but inexact account of its domain. (90-91)

These characteristics of economic theories and models have implications for several important areas: truth, prediction, explanation, and confirmation.  Is economics a scientific theory of existing observable economic phenomena?  Or is it an abstract, hypothetical model with only tangential implications for the observable social world?  Is economics an empirical science or a mathematical system?

Let's look at these questions in turn.  First, can we give a good interpretation of what it would mean to believe that an inexact theory or law is "true"?  Here is a possible answer: we may believe that there are real but unobservable causal processes that "drive" social phenomena.  To say that a social or economic theory is true is to say that it correctly identifies a real causal process -- whether or not that process operates with sufficient separation to give rise to strict empirical consequences.  Galilean laws of mechanics are true for falling objects, even if feathers follow unpredictable trajectories through turbulent gases.

Second, how can we reconcile the desire to use economic theories to make predictions about future states with the acknowledged inexactness of those theories and laws? If a theory includes hypotheses about underlying causal mechanisms that are true in the sense just mentioned, then a certain kind of prediction is justified as well: "in the absence of confounding causal factors, the presence of X will give rise to Y." But of course this is a useless predictive statement in the current situation, since the whole point is that economic processes rarely or never operate in isolation. So we are more or less compelled to conclude that theories based on inexact laws are not a useable ground for empirical prediction.

Third, in what sense do the deductive consequences of an inexact theory "explain" a given outcome -- either one that is consistent with those consequences or one that is inconsistent with the consequences? Here inexact laws are on stronger ground: after the fact, it is often possible to demonstrate that the mechanisms that led to an outcome are those specified by the theory. Explanation and prediction are not equIvalent. Natural selection explains the features of Darwin's finches -- but it doesn't permit prediction of future evolutionary change.

And finally, what is involved in trying to use empirical data to confirm or disconfirm an inexact theory?  Given that we have stipulated that the theory has false consequences, we can't use standard confirmation theory.  So what kind of empirical argument would help provide empirical evaluation of an inexact theory?  One possibility is that we might require that the predictions of the theory should fall within a certain range of the observable measurements -- which is implied by the idea of "approximately true" consequences.  But actually, it is possible that we might hold that a given theory is inexact, true, and wildly divergent from observed experience.  (This would be true of the application of classical mechanics to the problem of describing the behavior of light, irregular objects shot out of guns under water.)  Hausman confronts this type of issue when he asks why we should believe that the premises of general equilibrium theory are true. But here too there are alternatives, including piecemeal confirmation of individual causal hypotheses. Hausman refers to this possibility as a version of Mill's deductive method.

I take up some of these questions in my article, "Economic Models in Development Economics" link, included in On the Reliability of Economic Models: Essays in the Philosophy of Economics.  This article discusses some related questions about the reliability and applicability of computable general equilibrium models in application to the observed behavior of real economies.  Here are some concluding thoughts from that article concerning the empirical and logical features that are relevant to the assessment of CGE models:

"The general problem of the antecedent credibility of an economic model can be broken down into more specific questions concerning the validity, comprehensiveness, robustness, reliability, and autonomy of the model. I will define these concepts in the following terms.

• Validity is a measure of the degree to which the assumptions employed in the construction of the model are thought to correspond to the real processes underlying the phenomena represented by the model.
• Comprehensiveness is the degree to which the model is thought to succeed in capturing the major causal factors that influence the features of the behavior of the system in which we are interested.
• Robustness is a measure of the degree to which the results of the model persist under small perturbations in the settings of parameters, formulation of equations, etc.
• Autonomy refers to the stability of the model's results in face of variation of contextual factors.
• Reliability is a measure of the degree of confidence we can have in the data employed in setting the values of the parameters.

These are epistemic features of models that can be investigated more or less independently and prior to examination of the empirical success or failure of the predictions of the model."

(Hausman's book is virtually definitive in its formulation of the tasks and scope of the philosophy of economics.  When conjoined with the book he wrote with Michael McPherson, Economic Analysis, Moral Philosophy and Public Policy, the philosophy of economics itself becomes a "separate science": virtually all the important questions are raised throughout a bounded domain, and a reasonable set of theories are offered to answer those questions.)

Predictions

Image: Artillery, 1911. Roger de La Fresnaye. Metropolitan Museum, New York

In general I'm skeptical about the ability of the social sciences to offer predictions about future social developments. (In this respect I follow some of the instincts of Oskar Morgenstern in On the Accuracy of Economic Observations.) We have a hard time answering questions like these:

• How much will the first installment of TARP improve the availability of credit within three months?
• Will the introduction of UN peacekeeping units reduce ethnic killings in the Congo?
• Will the introduction of small high schools improve student performance in Chicago?
• Will China develop towards more democratic political institutions in the next twenty years?
• Will American cities witness another round of race riots in the next twenty years?
  

However, the situation isn't entirely negative, and there certainly are some social situations for which we can offer predictions in at least a probabilistic form. Here are some examples:

• The unemployment rate in Michigan will exceed 10% sometime in the next six months.
• Coalition casualties in the Afghanistan war will be greater in 2009 than in 2008.
• Illinois Governor Blogojevich will leave office within six months.
• Germany will be the world leader in solar energy research by 2020 (link).
• The Chinese government will act strategically to prevent emergence of regional independent labor organizations.
  

It is worth exploring the logic and function of prediction for a few lines. Fundamentally, it seems that prediction is related to the effort to forecast the effects of interventions, the trajectory of existing trends, and the likely strategies of powerful social actors. We often want to know what will be the net effect of introducing X into the social environment. (For example, what effect on economic development would result from a region's succeeding in increasing the high school graduation rate from 50% to 75%?) We may find it useful to project into the future some social trends that can be observed in the present. (Demographers' prediction that the United States will be a "majority-minority" population by 2042 falls in this category (link).) And we can often do quite a bit of rigorous reasoning about the likely actions of leaders, policy makers, and other powerful actors given what we know about their objectives and their beliefs. (We can try to forecast the outcome of the current impasse between Russia and Ukraine over natural gas by analyzing the strategic interests of both sets of decision-makers and the constraints to which they must respond.)

So the question is, what kinds of predictions can we make in the social realm? And what circumstances limit our ability to predict?

Predictions about social phenomena are based on a couple of basic modes of reasoning:

• extrapolation of current trends
• modeling of causal hypotheses about social mechanisms and structures
  
• reasoning about strategic actions likely to be taken by actors
• derivation of future states of a system from a set of laws

And predictions can be presented in a range of levels of precision, specificity, and confidence:

• prediction of a single event or outcome: the selected social system will be in state X at time T.
• prediction of the range within which a variable will fall: the selected social variable will fall within a range Q ±20%.
  
• prediction of the range of outcome scenarios that are most likely: "Given current level of unrest, rebellion 60%, everyday resistance 30%, resolution 10%"
  
• prediction of the direction of change: the variable of interest will increase/decrease over the specified time period
  
• prediction of the distribution of properties over a group of events/outcomes. X percent of interventions will show improvement of variable Y.
  

Here are some particular obstacles to reliable predictions in the social realm:

• unquantifiable causal hypotheses -- "small schools improve student performance". How large is the effect? How does it weigh in relation to other possible causal factors?
  
• indeterminate interaction effects -- how will school policy changes interact with rising unemployment to jointly influence school attendance and performance?
  
• open causal fields. What other currently unrecognized causal factors are in play?
  
• the occurrence of unpredictable exogenous events or processes (outbreak of disease)
• ceteris paribus conditions. These are frequently unsatisfied.
  

So where does all this leave us with respect to social predictions? A few points seem relatively clear.

Specific prediction of singular events and outcomes seems particularly difficult: the collapse of the Soviet Union, China's decision to cross the Yalu River in the Korean War, or the onset of the Great Depression were all surprises to the experts.

Projection of stable trends into the near future seems most defensible -- though of course we can give many examples of discontinuities in previously stable trends. Projection of trends over medium- and long-term is more uncertain -- given the likelihood of intervening changes of structure, behavior, and environment that will alter the trends over the extended time.

Predictions of limited social outcomes, couched in terms of a range of possibilities attached to estimates of probabilities and based on analysis of known causal and strategic processes, also appear defensible. The degree of confidence we can have in such predictions is limited by the possibility of unrecognized intervening causes and processes.

The idea of forecasting the total state of a social system given information about the current state of the system and a set of laws of change is entirely indefensible. This is unattainable; societies are not systems of variables linked by precise laws of transition.

Predictions

Image: Artillery, 1911. Roger de La Fresnaye. Metropolitan Museum, New York

In general I'm skeptical about the ability of the social sciences to offer predictions about future social developments. (In this respect I follow some of the instincts of Oskar Morgenstern in On the Accuracy of Economic Observations.) We have a hard time answering questions like these:

• How much will the first installment of TARP improve the availability of credit within three months?
• Will the introduction of UN peacekeeping units reduce ethnic killings in the Congo?
• Will the introduction of small high schools improve student performance in Chicago?
• Will China develop towards more democratic political institutions in the next twenty years?
• Will American cities witness another round of race riots in the next twenty years?
  

However, the situation isn't entirely negative, and there certainly are some social situations for which we can offer predictions in at least a probabilistic form. Here are some examples:

• The unemployment rate in Michigan will exceed 10% sometime in the next six months.
• Coalition casualties in the Afghanistan war will be greater in 2009 than in 2008.
• Illinois Governor Blogojevich will leave office within six months.
• Germany will be the world leader in solar energy research by 2020 (link).
• The Chinese government will act strategically to prevent emergence of regional independent labor organizations.
  

It is worth exploring the logic and function of prediction for a few lines. Fundamentally, it seems that prediction is related to the effort to forecast the effects of interventions, the trajectory of existing trends, and the likely strategies of powerful social actors. We often want to know what will be the net effect of introducing X into the social environment. (For example, what effect on economic development would result from a region's succeeding in increasing the high school graduation rate from 50% to 75%?) We may find it useful to project into the future some social trends that can be observed in the present. (Demographers' prediction that the United States will be a "majority-minority" population by 2042 falls in this category (link).) And we can often do quite a bit of rigorous reasoning about the likely actions of leaders, policy makers, and other powerful actors given what we know about their objectives and their beliefs. (We can try to forecast the outcome of the current impasse between Russia and Ukraine over natural gas by analyzing the strategic interests of both sets of decision-makers and the constraints to which they must respond.)

So the question is, what kinds of predictions can we make in the social realm? And what circumstances limit our ability to predict?

Predictions about social phenomena are based on a couple of basic modes of reasoning:

• extrapolation of current trends
• modeling of causal hypotheses about social mechanisms and structures
  
• reasoning about strategic actions likely to be taken by actors
• derivation of future states of a system from a set of laws

And predictions can be presented in a range of levels of precision, specificity, and confidence:

• prediction of a single event or outcome: the selected social system will be in state X at time T.
• prediction of the range within which a variable will fall: the selected social variable will fall within a range Q ±20%.
  
• prediction of the range of outcome scenarios that are most likely: "Given current level of unrest, rebellion 60%, everyday resistance 30%, resolution 10%"
  
• prediction of the direction of change: the variable of interest will increase/decrease over the specified time period
  
• prediction of the distribution of properties over a group of events/outcomes. X percent of interventions will show improvement of variable Y.
  

Here are some particular obstacles to reliable predictions in the social realm:

• unquantifiable causal hypotheses -- "small schools improve student performance". How large is the effect? How does it weigh in relation to other possible causal factors?
  
• indeterminate interaction effects -- how will school policy changes interact with rising unemployment to jointly influence school attendance and performance?
  
• open causal fields. What other currently unrecognized causal factors are in play?
  
• the occurrence of unpredictable exogenous events or processes (outbreak of disease)
• ceteris paribus conditions. These are frequently unsatisfied.
  

So where does all this leave us with respect to social predictions? A few points seem relatively clear.

Specific prediction of singular events and outcomes seems particularly difficult: the collapse of the Soviet Union, China's decision to cross the Yalu River in the Korean War, or the onset of the Great Depression were all surprises to the experts.

Projection of stable trends into the near future seems most defensible -- though of course we can give many examples of discontinuities in previously stable trends. Projection of trends over medium- and long-term is more uncertain -- given the likelihood of intervening changes of structure, behavior, and environment that will alter the trends over the extended time.

Predictions of limited social outcomes, couched in terms of a range of possibilities attached to estimates of probabilities and based on analysis of known causal and strategic processes, also appear defensible. The degree of confidence we can have in such predictions is limited by the possibility of unrecognized intervening causes and processes.

The idea of forecasting the total state of a social system given information about the current state of the system and a set of laws of change is entirely indefensible. This is unattainable; societies are not systems of variables linked by precise laws of transition.

Predictions

Image: Artillery, 1911. Roger de La Fresnaye. Metropolitan Museum, New York

In general I'm skeptical about the ability of the social sciences to offer predictions about future social developments. (In this respect I follow some of the instincts of Oskar Morgenstern in On the Accuracy of Economic Observations.) We have a hard time answering questions like these:

• How much will the first installment of TARP improve the availability of credit within three months?
• Will the introduction of UN peacekeeping units reduce ethnic killings in the Congo?
• Will the introduction of small high schools improve student performance in Chicago?
• Will China develop towards more democratic political institutions in the next twenty years?
• Will American cities witness another round of race riots in the next twenty years?
  

However, the situation isn't entirely negative, and there certainly are some social situations for which we can offer predictions in at least a probabilistic form. Here are some examples:

• The unemployment rate in Michigan will exceed 10% sometime in the next six months.
• Coalition casualties in the Afghanistan war will be greater in 2009 than in 2008.
• Illinois Governor Blogojevich will leave office within six months.
• Germany will be the world leader in solar energy research by 2020 (link).
• The Chinese government will act strategically to prevent emergence of regional independent labor organizations.
  

It is worth exploring the logic and function of prediction for a few lines. Fundamentally, it seems that prediction is related to the effort to forecast the effects of interventions, the trajectory of existing trends, and the likely strategies of powerful social actors. We often want to know what will be the net effect of introducing X into the social environment. (For example, what effect on economic development would result from a region's succeeding in increasing the high school graduation rate from 50% to 75%?) We may find it useful to project into the future some social trends that can be observed in the present. (Demographers' prediction that the United States will be a "majority-minority" population by 2042 falls in this category (link).) And we can often do quite a bit of rigorous reasoning about the likely actions of leaders, policy makers, and other powerful actors given what we know about their objectives and their beliefs. (We can try to forecast the outcome of the current impasse between Russia and Ukraine over natural gas by analyzing the strategic interests of both sets of decision-makers and the constraints to which they must respond.)

So the question is, what kinds of predictions can we make in the social realm? And what circumstances limit our ability to predict?

Predictions about social phenomena are based on a couple of basic modes of reasoning:

• extrapolation of current trends
• modeling of causal hypotheses about social mechanisms and structures
  
• reasoning about strategic actions likely to be taken by actors
• derivation of future states of a system from a set of laws

And predictions can be presented in a range of levels of precision, specificity, and confidence:

• prediction of a single event or outcome: the selected social system will be in state X at time T.
• prediction of the range within which a variable will fall: the selected social variable will fall within a range Q ±20%.
  
• prediction of the range of outcome scenarios that are most likely: "Given current level of unrest, rebellion 60%, everyday resistance 30%, resolution 10%"
  
• prediction of the direction of change: the variable of interest will increase/decrease over the specified time period
  
• prediction of the distribution of properties over a group of events/outcomes. X percent of interventions will show improvement of variable Y.
  

Here are some particular obstacles to reliable predictions in the social realm:

• unquantifiable causal hypotheses -- "small schools improve student performance". How large is the effect? How does it weigh in relation to other possible causal factors?
  
• indeterminate interaction effects -- how will school policy changes interact with rising unemployment to jointly influence school attendance and performance?
  
• open causal fields. What other currently unrecognized causal factors are in play?
  
• the occurrence of unpredictable exogenous events or processes (outbreak of disease)
• ceteris paribus conditions. These are frequently unsatisfied.
  

So where does all this leave us with respect to social predictions? A few points seem relatively clear.

Specific prediction of singular events and outcomes seems particularly difficult: the collapse of the Soviet Union, China's decision to cross the Yalu River in the Korean War, or the onset of the Great Depression were all surprises to the experts.

Projection of stable trends into the near future seems most defensible -- though of course we can give many examples of discontinuities in previously stable trends. Projection of trends over medium- and long-term is more uncertain -- given the likelihood of intervening changes of structure, behavior, and environment that will alter the trends over the extended time.

Predictions of limited social outcomes, couched in terms of a range of possibilities attached to estimates of probabilities and based on analysis of known causal and strategic processes, also appear defensible. The degree of confidence we can have in such predictions is limited by the possibility of unrecognized intervening causes and processes.

The idea of forecasting the total state of a social system given information about the current state of the system and a set of laws of change is entirely indefensible. This is unattainable; societies are not systems of variables linked by precise laws of transition.

Correspondence, abstraction, and realism

Science is generally concerned with two central semantic features of theories: truth of theoretical hypotheses and reliability of observational predictions. (Philosophers understand the concept of semantics as encompassing the relations between a sentence and the world: truth and reference. This understanding connects with the ordinary notion of semantics as meaning, in that the truth conditions of a sentence are thought to constitute the meaning of the sentence.) Truth involves a correspondence between hypothesis and the world; while predictions involve statements about the observable future behavior of a real system. Science is also concerned with epistemic values: warrant and justification. The warrant of a hypothesis is a measure of the degree to which available evidence permits us to conclude that the hypothesis is approximately true. A hypothesis may be true but unwarranted (that is, we may not have adequate evidence available to permit confidence in the truth of the hypothesis). Likewise, however, a hypothesis may be false but warranted (that is, available evidence may make the hypothesis highly credible, while it is in fact false). And every science possesses a set of standards of hypothesis evaluation on the basis of which practitioners assess the credibility of their theories--for example, testability, success in prediction, inter-theoretical support, simplicity, and the like.

The preceding suggests that there are several questions that arise in the assessment of scientific theories. First, we can ask whether a given hypothesis is a good approximation of the underlying social reality--that is, the approximate truth of the hypothesis. Likewise, we can ask whether the hypothesis gives rise to true predictions about the future behavior of the underlying social reality. Each of these questions falls on the side of the truth value of the hypothesis. Another set of questions concerns the warrant of the hypothesis: the strength of the evidence and theoretical grounds available to us on the basis of which we assign a degree of credibility to the hypothesis. Does available evidence give us reason to believe that the hypothesis is approximately true, and does available evidence give us reason to expect that the hypothesis's predictions are likely to be true? These questions are centrally epistemic; answers to them constitute the basis of our scientific confidence in the truth of the hypothesis and its predictions.

It is important to note that the question of the approximate truth of the hypothesis is separate from that of the approximate truth of its predictions. It is possible that the hypothesis is approximately true but its predictions are not. This might be the case because the ceteris paribus conditions are not satisfied, or because low precision of estimates for exogenous variables and parameters leads to indeterminate predictive consequences. Therefore it is possible that the warrant attaching to the approximate truth of the hypothesis and the reliability of its predictions may be different. It may be that we have good reason to believe that the hypothesis is a good approximation of the underlying economic reality, while at the same time we have little reason to rely on its predictions about the future behavior of the system. The warrant of the hypothesis is high on this account, while the warrant of its predictions is low.

Whatever position we arrive at concerning the possible truth or falsity of a given economic hypothesis, it is plain that this cannot be understood as literal descriptive truth. Economic hypotheses are not offered as full and detailed representations of the underlying economic reality. For a hypothesis unavoidably involves abstraction, in at least two ways. First, the hypothesis deliberately ignores some empirical characteristics and causal processes of the underlying economic reality. Just as a Newtonian hypothesis of the ballistics of projectiles ignores air resistance in order to focus on gravitational forces and the initial momentum of the projectile, so an economic hypothesis ignores differences in consumption behavior among members of functional defined income groups. Likewise, a hypothesis may abstract from regional or sectional differences in prices or wage rates within a national economy. Daniel Hausman provides an excellent discussion of the scope and limits of economic theories in The Inexact and Separate Science of Economics.

Another epistemically significant feature of social hypotheses is the difficulty of isolating causal factors in real social or economic systems. Hypotheses are generally subject to ceteris paribus conditions. Predictions and counterfactual assertions are advanced conditioned by the assumption that no other exogenous causal factors intervene; that is, the assertive content of the hypothesis is that the economic processes under analysis will unfold in the described manner absent intervening causal factors. But if there are intervening causal factors, then the overall behavior of the system may be indeterminate. In some cases it is possible to specify particularly salient interfering causal factors (e.g. political instability). But it is often necessary to incorporate open-ended ceteris paribus conditions as well.

Finally, social theories and hypotheses unavoidably make simplifying or idealizing assumptions about the populations, properties, and processes that they describe. Consumers are represented as possessing consistent and complete preference rankings; firms are represented as making optimizing choices of products and technologies; product markets are assumed to function perfectly; and so on.

Given, then, that hypotheses abstract from reality, in what sense does it make sense to ask whether a hypothesis is true? We must distinguish between truth and completeness, to start with. To say that a description of a system is true is not to say that it is a complete description. (A complete description provides a specification of the value of all state variables for the system--that is, all variables that have a causal role in the functioning of the system.) The fact that hypotheses are abstractive demonstrates only that they are incomplete, not that they are false. A description of a hockey puck's trajectory on the ice that assumes a frictionless surface is a true account of some of the causal factors at work: the Newtonian mechanics of the system. The assumption that the surface of the ice is frictionless is false; but in this particular system the overall behavior of the system (with friction) is sufficiently close to the abstract hypothesis (because frictional forces are small relative to other forces affecting the puck). In this case, then, we can say two things: first, the Newtonian hypothesis is exactly true as a description of the forces it directly represents, and second, it is approximately true as a description of the system as a whole (because the forces it ignores are small).

This account takes a strongly realist position on social theory, in that it characterizes truth in terms of correspondence to unobservable entities, processes, or properties. The presumption here is that social systems generally--and economic systems in particular--have objective unobservable characteristics which it is the task of social science theory to identify. The realist position is commonly challenged by some economists, however. Milton Friedman's famous argument for an instrumentalist interpretation of economic theory (Essays in Positive Economics) is highly unconvincing in this context. The instrumentalist position maintains that it is a mistake to understand theories as referring to real unobservable entities. Instead, theories are simply ways of systematizing observable characteristics of the phenomena under study; the only purpose of scientific theory is to serve as an instrument for prediction. Along these lines, Friedman argues that the realism of economic premises is irrelevant to the warrant of an economic theory; all that matters is the overall predictive success of the theory. The instrumentalist approach to the interpretation of economic theory, then, is highly unpersuasive as an interpretation of the epistemic standing of economic hypotheses. Instead, the realist position appears to be inescapable: we are forced to treat general equilibrium theory as a substantive empirical hypothesis about the real workings of competitive market systems, and our confidence in general equilibrium hypotheses is limited by our confidence in the approximate truth of the general equilibrium theory.

Correspondence, abstraction, and realism

Science is generally concerned with two central semantic features of theories: truth of theoretical hypotheses and reliability of observational predictions. (Philosophers understand the concept of semantics as encompassing the relations between a sentence and the world: truth and reference. This understanding connects with the ordinary notion of semantics as meaning, in that the truth conditions of a sentence are thought to constitute the meaning of the sentence.) Truth involves a correspondence between hypothesis and the world; while predictions involve statements about the observable future behavior of a real system. Science is also concerned with epistemic values: warrant and justification. The warrant of a hypothesis is a measure of the degree to which available evidence permits us to conclude that the hypothesis is approximately true. A hypothesis may be true but unwarranted (that is, we may not have adequate evidence available to permit confidence in the truth of the hypothesis). Likewise, however, a hypothesis may be false but warranted (that is, available evidence may make the hypothesis highly credible, while it is in fact false). And every science possesses a set of standards of hypothesis evaluation on the basis of which practitioners assess the credibility of their theories--for example, testability, success in prediction, inter-theoretical support, simplicity, and the like.

The preceding suggests that there are several questions that arise in the assessment of scientific theories. First, we can ask whether a given hypothesis is a good approximation of the underlying social reality--that is, the approximate truth of the hypothesis. Likewise, we can ask whether the hypothesis gives rise to true predictions about the future behavior of the underlying social reality. Each of these questions falls on the side of the truth value of the hypothesis. Another set of questions concerns the warrant of the hypothesis: the strength of the evidence and theoretical grounds available to us on the basis of which we assign a degree of credibility to the hypothesis. Does available evidence give us reason to believe that the hypothesis is approximately true, and does available evidence give us reason to expect that the hypothesis's predictions are likely to be true? These questions are centrally epistemic; answers to them constitute the basis of our scientific confidence in the truth of the hypothesis and its predictions.

It is important to note that the question of the approximate truth of the hypothesis is separate from that of the approximate truth of its predictions. It is possible that the hypothesis is approximately true but its predictions are not. This might be the case because the ceteris paribus conditions are not satisfied, or because low precision of estimates for exogenous variables and parameters leads to indeterminate predictive consequences. Therefore it is possible that the warrant attaching to the approximate truth of the hypothesis and the reliability of its predictions may be different. It may be that we have good reason to believe that the hypothesis is a good approximation of the underlying economic reality, while at the same time we have little reason to rely on its predictions about the future behavior of the system. The warrant of the hypothesis is high on this account, while the warrant of its predictions is low.

Whatever position we arrive at concerning the possible truth or falsity of a given economic hypothesis, it is plain that this cannot be understood as literal descriptive truth. Economic hypotheses are not offered as full and detailed representations of the underlying economic reality. For a hypothesis unavoidably involves abstraction, in at least two ways. First, the hypothesis deliberately ignores some empirical characteristics and causal processes of the underlying economic reality. Just as a Newtonian hypothesis of the ballistics of projectiles ignores air resistance in order to focus on gravitational forces and the initial momentum of the projectile, so an economic hypothesis ignores differences in consumption behavior among members of functional defined income groups. Likewise, a hypothesis may abstract from regional or sectional differences in prices or wage rates within a national economy. Daniel Hausman provides an excellent discussion of the scope and limits of economic theories in The Inexact and Separate Science of Economics.

Another epistemically significant feature of social hypotheses is the difficulty of isolating causal factors in real social or economic systems. Hypotheses are generally subject to ceteris paribus conditions. Predictions and counterfactual assertions are advanced conditioned by the assumption that no other exogenous causal factors intervene; that is, the assertive content of the hypothesis is that the economic processes under analysis will unfold in the described manner absent intervening causal factors. But if there are intervening causal factors, then the overall behavior of the system may be indeterminate. In some cases it is possible to specify particularly salient interfering causal factors (e.g. political instability). But it is often necessary to incorporate open-ended ceteris paribus conditions as well.

Finally, social theories and hypotheses unavoidably make simplifying or idealizing assumptions about the populations, properties, and processes that they describe. Consumers are represented as possessing consistent and complete preference rankings; firms are represented as making optimizing choices of products and technologies; product markets are assumed to function perfectly; and so on.

Given, then, that hypotheses abstract from reality, in what sense does it make sense to ask whether a hypothesis is true? We must distinguish between truth and completeness, to start with. To say that a description of a system is true is not to say that it is a complete description. (A complete description provides a specification of the value of all state variables for the system--that is, all variables that have a causal role in the functioning of the system.) The fact that hypotheses are abstractive demonstrates only that they are incomplete, not that they are false. A description of a hockey puck's trajectory on the ice that assumes a frictionless surface is a true account of some of the causal factors at work: the Newtonian mechanics of the system. The assumption that the surface of the ice is frictionless is false; but in this particular system the overall behavior of the system (with friction) is sufficiently close to the abstract hypothesis (because frictional forces are small relative to other forces affecting the puck). In this case, then, we can say two things: first, the Newtonian hypothesis is exactly true as a description of the forces it directly represents, and second, it is approximately true as a description of the system as a whole (because the forces it ignores are small).

This account takes a strongly realist position on social theory, in that it characterizes truth in terms of correspondence to unobservable entities, processes, or properties. The presumption here is that social systems generally--and economic systems in particular--have objective unobservable characteristics which it is the task of social science theory to identify. The realist position is commonly challenged by some economists, however. Milton Friedman's famous argument for an instrumentalist interpretation of economic theory (Essays in Positive Economics) is highly unconvincing in this context. The instrumentalist position maintains that it is a mistake to understand theories as referring to real unobservable entities. Instead, theories are simply ways of systematizing observable characteristics of the phenomena under study; the only purpose of scientific theory is to serve as an instrument for prediction. Along these lines, Friedman argues that the realism of economic premises is irrelevant to the warrant of an economic theory; all that matters is the overall predictive success of the theory. The instrumentalist approach to the interpretation of economic theory, then, is highly unpersuasive as an interpretation of the epistemic standing of economic hypotheses. Instead, the realist position appears to be inescapable: we are forced to treat general equilibrium theory as a substantive empirical hypothesis about the real workings of competitive market systems, and our confidence in general equilibrium hypotheses is limited by our confidence in the approximate truth of the general equilibrium theory.