Causal narratives about historical actors

A common kind of causal narrative employed by historians is to identify a set of key actors, key circumstances, and key resources; and then to treat a period of time as a flow of actions by the actors in response to each other and changing circumstances. We might describe this as "explanation of an outcome as cumulative result of actions by differently situated actors, within a specified set of institutions, resources, and environmental factors."

Individual actions make sense in the context, so we have explained their behavior at the micro level. But we can also "calculate" aggregate structural consequences of these actions, and we can thereby reach conclusions about how change in one set of structural conditions led to another set of structural changes through the flow of situated actors choosing their strategies. The powers and resources available to different groups of actors are different and must be carefully assessed by the historian.

This explanatory logic is illustrated in Emmanuel Wallerstein's account of the development of the so-called absolute monarchy in France (The Modern World-System I: Capitalist Agriculture and the Origins of the European World-Economy in the Sixteenth Century, With a New Prologue). Actors choose strategies based on the circumstances, alliances, and opponents that confront them. Actors include: king, lord, peasant, merchant, civil servant, bandit, clergy. Most of these actors also have collective organizations that function as actors at a group level. Economic crisis in the fourteenth and fifteenth centuries led officials of medieval states to broaden and extend their bureaucracies (134). Kings profited from disorder to extend their wealth at the expense of the nobles (135). Kings set more ambitious objectives for their states (135). Kings needed support from nobles and created parliaments. Kings used a variety of strategies to centralize power.

How did kings, who were the managers of the state machinery in the sixteenth century, strengthen themselves? They used four mechanisms: bureaucratization, monopoly of force, creation of legitimacy, and homogenization of the subject population. (136)

These actions aggregate into a new set of circumstances, a more effective set of centralized state powers, for the next period of play.

Institutions come into this kind of narrative in two ways. First, they are objective constraints and facts on the ground for the actors, leading actors to adjust their strategies. But second, institutions are built and modified through strategic actions and oppositions of the actors during a period of time. The king wants to strengthen the institutions of tax collection in the provinces; local lords oppose this effort. Each party deploys powers and opportunities to protect its interests. The resultant institutions are different from what either party would have designed.

Where do "causes" come into this kind of narrative? Climate change is a good example. Wallerstein considers the idea that climate change caused the emergence of a new set of institutions of land use (33 ff.). The mechanism is through the actions of the various stakeholders, competing and cooperating to adjust institutions to fit their current needs. Let us say the king is in a situation of greater power than the peasant or the lord. The king largely prevails in institutionalizing a new set of land use practices. One of the causes of the new institution is climate change, working through the strategic actions of the several groups of players. Or: Medieval landowners suffer income loss during a period of economic crisis; they recognize an income opportunity in enclosing public lands for private cultivation; peasants resist using traditional forms of protest; landowners generally have a power advantage and are supported by the state; landowners prevail. In this story, economic crisis causes change of land property relations, mediated by the strategic actions of key actors and groups. (Marx and Brenner tell different versions of this story; link, link.)

A different kind of example comes in at the level of the state. Wallerstein holds that the absolutist state caused the extension of the world trading system. What does he mean by this? He means that new state institutions created new powers and opportunities for several groups of actors, and the net result of these actor's strategies was to bring about a rapid increase in trade and associated military strategies. Again -- a macro cause of a macro outcome, flowing through an analysis of the strategies, interests, and powers of the historically situated actors.

This model of agent-based causal narratives seems to fit well into the methodology of agent-based simulations, with adjustments of the conditions of play from one iteration to the next. We would specify the goals and knowledge possessed by the actors. We would stipulate the institutional "geography" of the playing field. The institutions would define the powers possessed by each actor and the resources available for competition. We would represent alliances, competitions, and outcomes as they develop, noting that there is a stochastic and path-dependent nature to the unfolding of these scenarios.

Essentially the model for explaining social change and stability goes along these lines: actors act according to their interests and psychology. To explain a new outcome we need to identify either:

a structural circumstance or resource that significantly changed the situation of action for one or more groups;

Or:

a change in the conditions of agency in the actors themselves -- ideology, religion, new factual theories and beliefs.

Either of these changes can then account for a persistent pattern of behavior leading to a new social outcome. This framework of explanation fits well into the ontology of methodological localism and also leaves room for meso-level causal factors.

Causal narratives about historical actors

A common kind of causal narrative employed by historians is to identify a set of key actors, key circumstances, and key resources; and then to treat a period of time as a flow of actions by the actors in response to each other and changing circumstances. We might describe this as "explanation of an outcome as cumulative result of actions by differently situated actors, within a specified set of institutions, resources, and environmental factors."

Individual actions make sense in the context, so we have explained their behavior at the micro level. But we can also "calculate" aggregate structural consequences of these actions, and we can thereby reach conclusions about how change in one set of structural conditions led to another set of structural changes through the flow of situated actors choosing their strategies. The powers and resources available to different groups of actors are different and must be carefully assessed by the historian.

This explanatory logic is illustrated in Emmanuel Wallerstein's account of the development of the so-called absolute monarchy in France (The Modern World-System I: Capitalist Agriculture and the Origins of the European World-Economy in the Sixteenth Century, With a New Prologue). Actors choose strategies based on the circumstances, alliances, and opponents that confront them. Actors include: king, lord, peasant, merchant, civil servant, bandit, clergy. Most of these actors also have collective organizations that function as actors at a group level. Economic crisis in the fourteenth and fifteenth centuries led officials of medieval states to broaden and extend their bureaucracies (134). Kings profited from disorder to extend their wealth at the expense of the nobles (135). Kings set more ambitious objectives for their states (135). Kings needed support from nobles and created parliaments. Kings used a variety of strategies to centralize power.

How did kings, who were the managers of the state machinery in the sixteenth century, strengthen themselves? They used four mechanisms: bureaucratization, monopoly of force, creation of legitimacy, and homogenization of the subject population. (136)

These actions aggregate into a new set of circumstances, a more effective set of centralized state powers, for the next period of play.

Institutions come into this kind of narrative in two ways. First, they are objective constraints and facts on the ground for the actors, leading actors to adjust their strategies. But second, institutions are built and modified through strategic actions and oppositions of the actors during a period of time. The king wants to strengthen the institutions of tax collection in the provinces; local lords oppose this effort. Each party deploys powers and opportunities to protect its interests. The resultant institutions are different from what either party would have designed.

Where do "causes" come into this kind of narrative? Climate change is a good example. Wallerstein considers the idea that climate change caused the emergence of a new set of institutions of land use (33 ff.). The mechanism is through the actions of the various stakeholders, competing and cooperating to adjust institutions to fit their current needs. Let us say the king is in a situation of greater power than the peasant or the lord. The king largely prevails in institutionalizing a new set of land use practices. One of the causes of the new institution is climate change, working through the strategic actions of the several groups of players. Or: Medieval landowners suffer income loss during a period of economic crisis; they recognize an income opportunity in enclosing public lands for private cultivation; peasants resist using traditional forms of protest; landowners generally have a power advantage and are supported by the state; landowners prevail. In this story, economic crisis causes change of land property relations, mediated by the strategic actions of key actors and groups. (Marx and Brenner tell different versions of this story; link, link.)

A different kind of example comes in at the level of the state. Wallerstein holds that the absolutist state caused the extension of the world trading system. What does he mean by this? He means that new state institutions created new powers and opportunities for several groups of actors, and the net result of these actor's strategies was to bring about a rapid increase in trade and associated military strategies. Again -- a macro cause of a macro outcome, flowing through an analysis of the strategies, interests, and powers of the historically situated actors.

This model of agent-based causal narratives seems to fit well into the methodology of agent-based simulations, with adjustments of the conditions of play from one iteration to the next. We would specify the goals and knowledge possessed by the actors. We would stipulate the institutional "geography" of the playing field. The institutions would define the powers possessed by each actor and the resources available for competition. We would represent alliances, competitions, and outcomes as they develop, noting that there is a stochastic and path-dependent nature to the unfolding of these scenarios.

Essentially the model for explaining social change and stability goes along these lines: actors act according to their interests and psychology. To explain a new outcome we need to identify either:

a structural circumstance or resource that significantly changed the situation of action for one or more groups;

Or:

a change in the conditions of agency in the actors themselves -- ideology, religion, new factual theories and beliefs.

Either of these changes can then account for a persistent pattern of behavior leading to a new social outcome. This framework of explanation fits well into the ontology of methodological localism and also leaves room for meso-level causal factors.

Causal narratives about historical actors

A common kind of causal narrative employed by historians is to identify a set of key actors, key circumstances, and key resources; and then to treat a period of time as a flow of actions by the actors in response to each other and changing circumstances. We might describe this as "explanation of an outcome as cumulative result of actions by differently situated actors, within a specified set of institutions, resources, and environmental factors."

Individual actions make sense in the context, so we have explained their behavior at the micro level. But we can also "calculate" aggregate structural consequences of these actions, and we can thereby reach conclusions about how change in one set of structural conditions led to another set of structural changes through the flow of situated actors choosing their strategies. The powers and resources available to different groups of actors are different and must be carefully assessed by the historian.

This explanatory logic is illustrated in Emmanuel Wallerstein's account of the development of the so-called absolute monarchy in France (The Modern World-System I: Capitalist Agriculture and the Origins of the European World-Economy in the Sixteenth Century, With a New Prologue). Actors choose strategies based on the circumstances, alliances, and opponents that confront them. Actors include: king, lord, peasant, merchant, civil servant, bandit, clergy. Most of these actors also have collective organizations that function as actors at a group level. Economic crisis in the fourteenth and fifteenth centuries led officials of medieval states to broaden and extend their bureaucracies (134). Kings profited from disorder to extend their wealth at the expense of the nobles (135). Kings set more ambitious objectives for their states (135). Kings needed support from nobles and created parliaments. Kings used a variety of strategies to centralize power.

How did kings, who were the managers of the state machinery in the sixteenth century, strengthen themselves? They used four mechanisms: bureaucratization, monopoly of force, creation of legitimacy, and homogenization of the subject population. (136)

These actions aggregate into a new set of circumstances, a more effective set of centralized state powers, for the next period of play.

Institutions come into this kind of narrative in two ways. First, they are objective constraints and facts on the ground for the actors, leading actors to adjust their strategies. But second, institutions are built and modified through strategic actions and oppositions of the actors during a period of time. The king wants to strengthen the institutions of tax collection in the provinces; local lords oppose this effort. Each party deploys powers and opportunities to protect its interests. The resultant institutions are different from what either party would have designed.

Where do "causes" come into this kind of narrative? Climate change is a good example. Wallerstein considers the idea that climate change caused the emergence of a new set of institutions of land use (33 ff.). The mechanism is through the actions of the various stakeholders, competing and cooperating to adjust institutions to fit their current needs. Let us say the king is in a situation of greater power than the peasant or the lord. The king largely prevails in institutionalizing a new set of land use practices. One of the causes of the new institution is climate change, working through the strategic actions of the several groups of players. Or: Medieval landowners suffer income loss during a period of economic crisis; they recognize an income opportunity in enclosing public lands for private cultivation; peasants resist using traditional forms of protest; landowners generally have a power advantage and are supported by the state; landowners prevail. In this story, economic crisis causes change of land property relations, mediated by the strategic actions of key actors and groups. (Marx and Brenner tell different versions of this story; link, link.)

A different kind of example comes in at the level of the state. Wallerstein holds that the absolutist state caused the extension of the world trading system. What does he mean by this? He means that new state institutions created new powers and opportunities for several groups of actors, and the net result of these actor's strategies was to bring about a rapid increase in trade and associated military strategies. Again -- a macro cause of a macro outcome, flowing through an analysis of the strategies, interests, and powers of the historically situated actors.

This model of agent-based causal narratives seems to fit well into the methodology of agent-based simulations, with adjustments of the conditions of play from one iteration to the next. We would specify the goals and knowledge possessed by the actors. We would stipulate the institutional "geography" of the playing field. The institutions would define the powers possessed by each actor and the resources available for competition. We would represent alliances, competitions, and outcomes as they develop, noting that there is a stochastic and path-dependent nature to the unfolding of these scenarios.

Essentially the model for explaining social change and stability goes along these lines: actors act according to their interests and psychology. To explain a new outcome we need to identify either:

a structural circumstance or resource that significantly changed the situation of action for one or more groups;

Or:

a change in the conditions of agency in the actors themselves -- ideology, religion, new factual theories and beliefs.

Either of these changes can then account for a persistent pattern of behavior leading to a new social outcome. This framework of explanation fits well into the ontology of methodological localism and also leaves room for meso-level causal factors.

Criteria for assessing economic models

How can we assess the epistemic warrant of an economic model that purports to represent some aspects of economic reality?  The general problem of assessing the credibility of an economic model can be broken down into more specific questions concerning the validity, comprehensiveness, robustness, reliability, and autonomy of the model. Here are initial definitions of these concepts.

• 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 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.

Let us look more closely at these standards of adequacy. The discussion of realism elsewhere suggests that we may attempt to validate the model deductively, by examining each of the assumptions underlying construction of the model for its plausibility or realism (link). (This resembles Mill's "deductive method" of theory evaluation.) Economists are highly confident in the underlying general equilibrium theory. The theory is incomplete (or, in Daniel Hausman's language, inexact; link), in that economic outcomes are not wholly determined by purely economic forces. But within its scope economists are confident that the theory identifies the main causal processes: an equilibration of supply and demand through market-determined prices.

Validity can be assessed through direct inspection of the substantive economic assumptions of the model: the formulation of consumer and firm behavior, the representation of production and consumption functions, the closure rules, and the like. To the extent that the particular formulation embodied in the model is supported by accepted economic theory, the validity of the model is enhanced. On the other hand, if particular formulations appear to be ad hoc (introduced, perhaps, to make the problem more tractable), the validity of the model is reduced. If, for example, the model assumes linear demand functions and we judge that this is a highly unrealistic assumption about the real underlying demand functions, then we will have less confidence in the predictive results of the model.

Unfortunately, there can be no fixed standard of evaluation concerning the validity of a model. All models make simplifying and idealizing assumptions; so to that extent they deviate from literal realism. And the question of whether a given idealization is felicitous or not cannot always be resolved on antecedent theoretical grounds; instead, it is necessary to look at the overall empirical adequacy of the model. The adequacy of the assumption of fixed coefficients of production cannot be assessed a priori; in some contexts and for some purposes it is a reasonable approximation of the economic reality, while in other cases it introduces unacceptable distortion of the actual economic processes (when input substitution is extensive). What can be said concerning the validity of a model's assumptions is rather minimal but not entirely vacuous. The assumptions should be consistent with existing economic theory; they should be reasonable and motivated formulations of background economic principles; and they should be implemented in a mathematically acceptable fashion.

Comprehensiveness too is a weak constraint on economic models. It is plain that all economic theories and models disregard some causal factors in order to isolate the workings of specific economic mechanisms; moreover, there will always be economic forces that have not been represented within the model. So judgment of the comprehensiveness of a model depends on a qualitative assessment of the relative importance of various economic and non-economic factors in the particular system under analysis. If a given factor seems to be economically important (e.g. input substitution) but unrepresented within the model, then the model loses points on comprehensiveness.

Robustness can be directly assessed through a technique widely used by economists, sensitivity analysis. The model is run a large number of times, varying the values assigned to parameters (reflecting the range of uncertainty in estimates or observations). If the model continues to have qualitatively similar findings, it is said to be robust. If solutions vary wildly under small perturbations of the parameter settings, the model is rightly thought to be a poor indicator of the underlying economic mechanisms.

Autonomy is the theoretical equivalent of robustness. It is a measure of the stability of the model under changes of assumptions about the causal background of the system. If the model's results are highly sensitive to changes in the environment within which the modeled processes take place, then we should be suspicious of the results of the model.

Assessment of reliability is also somewhat more straightforward than comprehensiveness and validity. The empirical data used to set parameters and exogenous variables have been gathered through specific well-understood procedures, and it is mandatory that we give some account of the precision of the resulting data.

Note that reliability and robustness interact; if we find that the model is highly robust with respect to a particular set of parameters, then the unreliability of estimates of those parameters will not have much effect on the reliability of the model itself. In this case it is enough to have "stylized facts" governing the parameters that are used: roughly 60% of workers' income is spent on food, 0% is saved, etc.

Failures along each of these lines can be illustrated easily.

1. The model assumes that prices are determined on the basis of markup pricing (costs plus a fixed exogenous markup rate and wage). In fact, however, we might believe (along neoclassical lines) that prices, wages, and the profit rate are all endogenous, so that markup pricing misrepresents the underlying price mechanism. This would be a failure of validity; the model is premised on assumptions that may not hold. 
2. The model is premised on a two-sector analysis of the economy. However, energy production and consumption turn out to be economically crucial factors in the performance of the economy, and these effects are overlooked unless we represent the energy sector separately. This would be a failure of comprehensiveness; there is an economically significant factor that is not represented in the model. 
3. We rerun the model assuming a slightly altered set of production coefficients, and we find that the predictions are substantially different: the increase in income is only 33% of what it was, and deficits are only half what they were. This is a failure of robustness; once we know that the model is extremely sensitive to variations in the parameters, we have strong reason to doubt its predictions. The accuracy of measurement of parameters is limited, so we can be confident that remeasurement would produce different values. So we can in turn expect that the simulation will arrive at different values for the endogenous variables. 
4. Suppose that our model of income distribution in a developing economy is premised on the international trading arrangements embodied in GATT. The model is designed to represent the domestic causal relations between food subsidies and the pattern of income distribution across classes. If the results of the model change substantially upon dropping the GATT assumption, then the model is not autonomous with respect to international trading arrangements. 
5. Finally, we examine the data underlying the consumption functions and we find that these derive from one household study in one Mexican state, involving 300 households. Moreover, we determine that the model is sensitive to the parameters defining consumption functions. On this scenario we have little reason to expect that the estimates derived from the household study are reliable estimates of consumption in all social classes all across Mexico; and therefore we have little reason to depend on the predictions of the model. This is a failure of reliability. 

These factors--validity, comprehensiveness, robustness, autonomy, and reliability--figure into our assessment of the antecedent credibility of a given model. If the model is judged to be reasonably valid and comprehensive; if it appears to be fairly robust and autonomous; and if the empirical data on which it rests appears to be reliable; then we have reason to believe that the model is a reasonable representation of the underlying economic reality. But this deductive validation of the model does not take us far enough. These are reasons to have a priori confidence in the model. But we need as well to have a basis for a posteriori confidence in the particular results of this specific model. And since there are many well-known ways in which a generally well-constructed model can nonetheless miss the mark--incompleteness of the causal field, failure of ceteris paribus clauses, poor data or poor estimates of the exogenous variables and parameters, proliferation of error to the point where the solution has no value, and path-dependence of the equilibrium solution--we need to have some way of empirically evaluating the results of the model.

(Here is an application of these ideas to computable general equilibrium (CGE) models in an article published in On the Reliability of Economic Models: Essays in the Philosophy of Economics; link.  See also Lance Taylor's reply and discussion in the same volume.)

Criteria for assessing economic models

How can we assess the epistemic warrant of an economic model that purports to represent some aspects of economic reality?  The general problem of assessing the credibility of an economic model can be broken down into more specific questions concerning the validity, comprehensiveness, robustness, reliability, and autonomy of the model. Here are initial definitions of these concepts.

• 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 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.

Let us look more closely at these standards of adequacy. The discussion of realism elsewhere suggests that we may attempt to validate the model deductively, by examining each of the assumptions underlying construction of the model for its plausibility or realism (link). (This resembles Mill's "deductive method" of theory evaluation.) Economists are highly confident in the underlying general equilibrium theory. The theory is incomplete (or, in Daniel Hausman's language, inexact; link), in that economic outcomes are not wholly determined by purely economic forces. But within its scope economists are confident that the theory identifies the main causal processes: an equilibration of supply and demand through market-determined prices.

Validity can be assessed through direct inspection of the substantive economic assumptions of the model: the formulation of consumer and firm behavior, the representation of production and consumption functions, the closure rules, and the like. To the extent that the particular formulation embodied in the model is supported by accepted economic theory, the validity of the model is enhanced. On the other hand, if particular formulations appear to be ad hoc (introduced, perhaps, to make the problem more tractable), the validity of the model is reduced. If, for example, the model assumes linear demand functions and we judge that this is a highly unrealistic assumption about the real underlying demand functions, then we will have less confidence in the predictive results of the model.

Unfortunately, there can be no fixed standard of evaluation concerning the validity of a model. All models make simplifying and idealizing assumptions; so to that extent they deviate from literal realism. And the question of whether a given idealization is felicitous or not cannot always be resolved on antecedent theoretical grounds; instead, it is necessary to look at the overall empirical adequacy of the model. The adequacy of the assumption of fixed coefficients of production cannot be assessed a priori; in some contexts and for some purposes it is a reasonable approximation of the economic reality, while in other cases it introduces unacceptable distortion of the actual economic processes (when input substitution is extensive). What can be said concerning the validity of a model's assumptions is rather minimal but not entirely vacuous. The assumptions should be consistent with existing economic theory; they should be reasonable and motivated formulations of background economic principles; and they should be implemented in a mathematically acceptable fashion.

Comprehensiveness too is a weak constraint on economic models. It is plain that all economic theories and models disregard some causal factors in order to isolate the workings of specific economic mechanisms; moreover, there will always be economic forces that have not been represented within the model. So judgment of the comprehensiveness of a model depends on a qualitative assessment of the relative importance of various economic and non-economic factors in the particular system under analysis. If a given factor seems to be economically important (e.g. input substitution) but unrepresented within the model, then the model loses points on comprehensiveness.

Robustness can be directly assessed through a technique widely used by economists, sensitivity analysis. The model is run a large number of times, varying the values assigned to parameters (reflecting the range of uncertainty in estimates or observations). If the model continues to have qualitatively similar findings, it is said to be robust. If solutions vary wildly under small perturbations of the parameter settings, the model is rightly thought to be a poor indicator of the underlying economic mechanisms.

Autonomy is the theoretical equivalent of robustness. It is a measure of the stability of the model under changes of assumptions about the causal background of the system. If the model's results are highly sensitive to changes in the environment within which the modeled processes take place, then we should be suspicious of the results of the model.

Assessment of reliability is also somewhat more straightforward than comprehensiveness and validity. The empirical data used to set parameters and exogenous variables have been gathered through specific well-understood procedures, and it is mandatory that we give some account of the precision of the resulting data.

Note that reliability and robustness interact; if we find that the model is highly robust with respect to a particular set of parameters, then the unreliability of estimates of those parameters will not have much effect on the reliability of the model itself. In this case it is enough to have "stylized facts" governing the parameters that are used: roughly 60% of workers' income is spent on food, 0% is saved, etc.

Failures along each of these lines can be illustrated easily.

1. The model assumes that prices are determined on the basis of markup pricing (costs plus a fixed exogenous markup rate and wage). In fact, however, we might believe (along neoclassical lines) that prices, wages, and the profit rate are all endogenous, so that markup pricing misrepresents the underlying price mechanism. This would be a failure of validity; the model is premised on assumptions that may not hold. 
2. The model is premised on a two-sector analysis of the economy. However, energy production and consumption turn out to be economically crucial factors in the performance of the economy, and these effects are overlooked unless we represent the energy sector separately. This would be a failure of comprehensiveness; there is an economically significant factor that is not represented in the model. 
3. We rerun the model assuming a slightly altered set of production coefficients, and we find that the predictions are substantially different: the increase in income is only 33% of what it was, and deficits are only half what they were. This is a failure of robustness; once we know that the model is extremely sensitive to variations in the parameters, we have strong reason to doubt its predictions. The accuracy of measurement of parameters is limited, so we can be confident that remeasurement would produce different values. So we can in turn expect that the simulation will arrive at different values for the endogenous variables. 
4. Suppose that our model of income distribution in a developing economy is premised on the international trading arrangements embodied in GATT. The model is designed to represent the domestic causal relations between food subsidies and the pattern of income distribution across classes. If the results of the model change substantially upon dropping the GATT assumption, then the model is not autonomous with respect to international trading arrangements. 
5. Finally, we examine the data underlying the consumption functions and we find that these derive from one household study in one Mexican state, involving 300 households. Moreover, we determine that the model is sensitive to the parameters defining consumption functions. On this scenario we have little reason to expect that the estimates derived from the household study are reliable estimates of consumption in all social classes all across Mexico; and therefore we have little reason to depend on the predictions of the model. This is a failure of reliability. 

These factors--validity, comprehensiveness, robustness, autonomy, and reliability--figure into our assessment of the antecedent credibility of a given model. If the model is judged to be reasonably valid and comprehensive; if it appears to be fairly robust and autonomous; and if the empirical data on which it rests appears to be reliable; then we have reason to believe that the model is a reasonable representation of the underlying economic reality. But this deductive validation of the model does not take us far enough. These are reasons to have a priori confidence in the model. But we need as well to have a basis for a posteriori confidence in the particular results of this specific model. And since there are many well-known ways in which a generally well-constructed model can nonetheless miss the mark--incompleteness of the causal field, failure of ceteris paribus clauses, poor data or poor estimates of the exogenous variables and parameters, proliferation of error to the point where the solution has no value, and path-dependence of the equilibrium solution--we need to have some way of empirically evaluating the results of the model.

(Here is an application of these ideas to computable general equilibrium (CGE) models in an article published in On the Reliability of Economic Models: Essays in the Philosophy of Economics; link.  See also Lance Taylor's reply and discussion in the same volume.)

Criteria for assessing economic models

How can we assess the epistemic warrant of an economic model that purports to represent some aspects of economic reality?  The general problem of assessing the credibility of an economic model can be broken down into more specific questions concerning the validity, comprehensiveness, robustness, reliability, and autonomy of the model. Here are initial definitions of these concepts.

• 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 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.

Let us look more closely at these standards of adequacy. The discussion of realism elsewhere suggests that we may attempt to validate the model deductively, by examining each of the assumptions underlying construction of the model for its plausibility or realism (link). (This resembles Mill's "deductive method" of theory evaluation.) Economists are highly confident in the underlying general equilibrium theory. The theory is incomplete (or, in Daniel Hausman's language, inexact; link), in that economic outcomes are not wholly determined by purely economic forces. But within its scope economists are confident that the theory identifies the main causal processes: an equilibration of supply and demand through market-determined prices.

Validity can be assessed through direct inspection of the substantive economic assumptions of the model: the formulation of consumer and firm behavior, the representation of production and consumption functions, the closure rules, and the like. To the extent that the particular formulation embodied in the model is supported by accepted economic theory, the validity of the model is enhanced. On the other hand, if particular formulations appear to be ad hoc (introduced, perhaps, to make the problem more tractable), the validity of the model is reduced. If, for example, the model assumes linear demand functions and we judge that this is a highly unrealistic assumption about the real underlying demand functions, then we will have less confidence in the predictive results of the model.

Unfortunately, there can be no fixed standard of evaluation concerning the validity of a model. All models make simplifying and idealizing assumptions; so to that extent they deviate from literal realism. And the question of whether a given idealization is felicitous or not cannot always be resolved on antecedent theoretical grounds; instead, it is necessary to look at the overall empirical adequacy of the model. The adequacy of the assumption of fixed coefficients of production cannot be assessed a priori; in some contexts and for some purposes it is a reasonable approximation of the economic reality, while in other cases it introduces unacceptable distortion of the actual economic processes (when input substitution is extensive). What can be said concerning the validity of a model's assumptions is rather minimal but not entirely vacuous. The assumptions should be consistent with existing economic theory; they should be reasonable and motivated formulations of background economic principles; and they should be implemented in a mathematically acceptable fashion.

Comprehensiveness too is a weak constraint on economic models. It is plain that all economic theories and models disregard some causal factors in order to isolate the workings of specific economic mechanisms; moreover, there will always be economic forces that have not been represented within the model. So judgment of the comprehensiveness of a model depends on a qualitative assessment of the relative importance of various economic and non-economic factors in the particular system under analysis. If a given factor seems to be economically important (e.g. input substitution) but unrepresented within the model, then the model loses points on comprehensiveness.

Robustness can be directly assessed through a technique widely used by economists, sensitivity analysis. The model is run a large number of times, varying the values assigned to parameters (reflecting the range of uncertainty in estimates or observations). If the model continues to have qualitatively similar findings, it is said to be robust. If solutions vary wildly under small perturbations of the parameter settings, the model is rightly thought to be a poor indicator of the underlying economic mechanisms.

Autonomy is the theoretical equivalent of robustness. It is a measure of the stability of the model under changes of assumptions about the causal background of the system. If the model's results are highly sensitive to changes in the environment within which the modeled processes take place, then we should be suspicious of the results of the model.

Assessment of reliability is also somewhat more straightforward than comprehensiveness and validity. The empirical data used to set parameters and exogenous variables have been gathered through specific well-understood procedures, and it is mandatory that we give some account of the precision of the resulting data.

Note that reliability and robustness interact; if we find that the model is highly robust with respect to a particular set of parameters, then the unreliability of estimates of those parameters will not have much effect on the reliability of the model itself. In this case it is enough to have "stylized facts" governing the parameters that are used: roughly 60% of workers' income is spent on food, 0% is saved, etc.

Failures along each of these lines can be illustrated easily.

1. The model assumes that prices are determined on the basis of markup pricing (costs plus a fixed exogenous markup rate and wage). In fact, however, we might believe (along neoclassical lines) that prices, wages, and the profit rate are all endogenous, so that markup pricing misrepresents the underlying price mechanism. This would be a failure of validity; the model is premised on assumptions that may not hold. 
2. The model is premised on a two-sector analysis of the economy. However, energy production and consumption turn out to be economically crucial factors in the performance of the economy, and these effects are overlooked unless we represent the energy sector separately. This would be a failure of comprehensiveness; there is an economically significant factor that is not represented in the model. 
3. We rerun the model assuming a slightly altered set of production coefficients, and we find that the predictions are substantially different: the increase in income is only 33% of what it was, and deficits are only half what they were. This is a failure of robustness; once we know that the model is extremely sensitive to variations in the parameters, we have strong reason to doubt its predictions. The accuracy of measurement of parameters is limited, so we can be confident that remeasurement would produce different values. So we can in turn expect that the simulation will arrive at different values for the endogenous variables. 
4. Suppose that our model of income distribution in a developing economy is premised on the international trading arrangements embodied in GATT. The model is designed to represent the domestic causal relations between food subsidies and the pattern of income distribution across classes. If the results of the model change substantially upon dropping the GATT assumption, then the model is not autonomous with respect to international trading arrangements. 
5. Finally, we examine the data underlying the consumption functions and we find that these derive from one household study in one Mexican state, involving 300 households. Moreover, we determine that the model is sensitive to the parameters defining consumption functions. On this scenario we have little reason to expect that the estimates derived from the household study are reliable estimates of consumption in all social classes all across Mexico; and therefore we have little reason to depend on the predictions of the model. This is a failure of reliability. 

These factors--validity, comprehensiveness, robustness, autonomy, and reliability--figure into our assessment of the antecedent credibility of a given model. If the model is judged to be reasonably valid and comprehensive; if it appears to be fairly robust and autonomous; and if the empirical data on which it rests appears to be reliable; then we have reason to believe that the model is a reasonable representation of the underlying economic reality. But this deductive validation of the model does not take us far enough. These are reasons to have a priori confidence in the model. But we need as well to have a basis for a posteriori confidence in the particular results of this specific model. And since there are many well-known ways in which a generally well-constructed model can nonetheless miss the mark--incompleteness of the causal field, failure of ceteris paribus clauses, poor data or poor estimates of the exogenous variables and parameters, proliferation of error to the point where the solution has no value, and path-dependence of the equilibrium solution--we need to have some way of empirically evaluating the results of the model.

(Here is an application of these ideas to computable general equilibrium (CGE) models in an article published in On the Reliability of Economic Models: Essays in the Philosophy of Economics; link.  See also Lance Taylor's reply and discussion in the same volume.)

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.)

Modest predictions in history

Image: the owl of Minerva

In spite of their reputations as historical determinists, Hegel and Marx each had their own versions of skepticism about "learning from history" -- in particular, the possibility of predicting the future based on historical knowledge. Notwithstanding his view that history embodies reason, Hegel is famous for his idea in the Philosophy of Right: "When philosophy paints its grey in grey then has a shape of life grown old. By philosophy's grey in grey it cannot be rejuvenated but only understood. The owl of Minerva spreads its wings only with the falling of dusk." And Marx puts the point more sardonically in the Eighteenth Brumaire: "Hegel remarks somewhere that all great world-historic facts and personages appear, so to speak, twice. He forgot to add: the first time as tragedy, the second time as farce." Both, then, cast specific doubt on the idea that history presents us with general patterns that can be projected into the future. Marx's remarks to Vera Zasulich about the prospects for communist revolution in Russia are instructive: "I thus expressly limited the 'historical inevitability' of this process to the countries of Western Europe."

This is a view I agree with very profoundly: history is contingent, there are always alternative pathways that might have been taken, and history has no general plan. So -- no grand predictions in history.

But then we have to ask a different sort of question. Specifically -- what kinds of predictions or projections are possible in history? And what is the intellectual base of grounded historical predictions? Here are a few predictions that seem to be supportable, drawn from recent postings on UnderstandingSociety:

• The Alsatian language is likely to disappear as a functioning medium of communication in Alsace within the next fifty years.
• Labor unrest in China will intensify over the next ten years.
• Social unrest will continue to occur over the next decade in Thailand, with a gradual increase in influence to dispossessed groups (red shirts).
• Large and deadly technology failures will occur in Europe and the United States in the next decade.
• Social movements will arise more frequently and more adaptively as a result of the use of social media (twitter, blogs, facebook, email).
• Conflicts between Arabs and Jews in East Jerusalem will continue to deepen in the next ten years as a consequence of the practical politics of land use and reclamation in the city.
  

Several things are apparent when we consider these predictions. First, they are limited in scope; they are small-scale features of the historical drama. Second, they depend on specific and identifiable social circumstances, along with clear ideas about social mechanisms connecting the present to the fruture. Third, they are at least by implication probabilistic; they indicate likelihoods rather than inevitabilities. Fourth, they imply the existence of ceteris paribus conditions: "Absent intervening factors, such-and-so is likely to occur." But, finally, they all appear to be intellectually justifiable. They may not be true, but they can be grounded in an empirically and historically justified analysis of the mechanisms that produce social change, and a model projecting the future effects of those mechanisms in combination.

The heart of prediction is our ability to identify dynamic processes and mechanisms that are at work in the present, and our ability to project their effects into the future. Modest predictions are those that single out fairly humdrum current processes in specific detail, and derive some expectations about how these processes will play out in the relatively short run. Grand predictions, on the other hand, purport to discover wide and encompassing patterns of development and then to extrapolate their civilizational consequences over a very long period. A modest prediction about China is the expectation that labor protest will intensify over the next ten years. A grand prediction about China is that it will become the dominant economic and military superpower of the late twenty-first century. We can have a fair degree of confidence in the first type of prediction; whereas there are vastly too many possible branches in history, too many "countervailing tendencies," too many accidents and contingencies, that may occur to give us any confidence in the latter prediction.

Ceteris paribus conditions are unavoidable in formulating historical expectations about the future, because social change is inherently complex and multi-causal. So even if it is case that a given process, accurately described in the present, creates a tendency for a certain kind of result -- it remains the case that there may well be other processes at work that will offset this result. The tendency of powerful agents to seize opportunities for enhancing their wealth through processes of urban development implies a certain kind of urban geography in the future; but this outcome might be offset by a genuinely robust and sustained citizens' movement at the city council level.

The idea that historical predictions are generally probabilistic is partly a consequence of the fact of the existence of unknown ceteris paribus conditions. But it is also, more fundamentally, a consequence of the fact that social causation itself is almost always probabilistic. If we say that rising conflict over important resources (X) is a cause of inter-group violence (Y), we don't mean that X is necessarily followed by Y; instead, we mean that X raises the likelihood of the occurrence of Y.

So two conclusions seem justified. First, there is a perfectly valid intellectual role for making historical predictions. But these need to be modest predictions: limited in scope, closely tied to theories of existing social mechanisms, and accompanied by ceteris paribus conditions. And second, grand predictions should be treated with great suspicion. At their best, they depend on identifying a few existing mechanisms and processes; but the fact of multi-causal historical change, the fact of the compounding of uncertainties, and the fact of the unpredictability of complex systems should all make us dubious about large and immodest claims about the future. For the big answers, we really have to wait for the owl of Minerva to spread her wings.

Modest predictions in history

Image: the owl of Minerva

In spite of their reputations as historical determinists, Hegel and Marx each had their own versions of skepticism about "learning from history" -- in particular, the possibility of predicting the future based on historical knowledge. Notwithstanding his view that history embodies reason, Hegel is famous for his idea in the Philosophy of Right: "When philosophy paints its grey in grey then has a shape of life grown old. By philosophy's grey in grey it cannot be rejuvenated but only understood. The owl of Minerva spreads its wings only with the falling of dusk." And Marx puts the point more sardonically in the Eighteenth Brumaire: "Hegel remarks somewhere that all great world-historic facts and personages appear, so to speak, twice. He forgot to add: the first time as tragedy, the second time as farce." Both, then, cast specific doubt on the idea that history presents us with general patterns that can be projected into the future. Marx's remarks to Vera Zasulich about the prospects for communist revolution in Russia are instructive: "I thus expressly limited the 'historical inevitability' of this process to the countries of Western Europe."

This is a view I agree with very profoundly: history is contingent, there are always alternative pathways that might have been taken, and history has no general plan. So -- no grand predictions in history.

But then we have to ask a different sort of question. Specifically -- what kinds of predictions or projections are possible in history? And what is the intellectual base of grounded historical predictions? Here are a few predictions that seem to be supportable, drawn from recent postings on UnderstandingSociety:

• The Alsatian language is likely to disappear as a functioning medium of communication in Alsace within the next fifty years.
• Labor unrest in China will intensify over the next ten years.
• Social unrest will continue to occur over the next decade in Thailand, with a gradual increase in influence to dispossessed groups (red shirts).
• Large and deadly technology failures will occur in Europe and the United States in the next decade.
• Social movements will arise more frequently and more adaptively as a result of the use of social media (twitter, blogs, facebook, email).
• Conflicts between Arabs and Jews in East Jerusalem will continue to deepen in the next ten years as a consequence of the practical politics of land use and reclamation in the city.
  

Several things are apparent when we consider these predictions. First, they are limited in scope; they are small-scale features of the historical drama. Second, they depend on specific and identifiable social circumstances, along with clear ideas about social mechanisms connecting the present to the fruture. Third, they are at least by implication probabilistic; they indicate likelihoods rather than inevitabilities. Fourth, they imply the existence of ceteris paribus conditions: "Absent intervening factors, such-and-so is likely to occur." But, finally, they all appear to be intellectually justifiable. They may not be true, but they can be grounded in an empirically and historically justified analysis of the mechanisms that produce social change, and a model projecting the future effects of those mechanisms in combination.

The heart of prediction is our ability to identify dynamic processes and mechanisms that are at work in the present, and our ability to project their effects into the future. Modest predictions are those that single out fairly humdrum current processes in specific detail, and derive some expectations about how these processes will play out in the relatively short run. Grand predictions, on the other hand, purport to discover wide and encompassing patterns of development and then to extrapolate their civilizational consequences over a very long period. A modest prediction about China is the expectation that labor protest will intensify over the next ten years. A grand prediction about China is that it will become the dominant economic and military superpower of the late twenty-first century. We can have a fair degree of confidence in the first type of prediction; whereas there are vastly too many possible branches in history, too many "countervailing tendencies," too many accidents and contingencies, that may occur to give us any confidence in the latter prediction.

Ceteris paribus conditions are unavoidable in formulating historical expectations about the future, because social change is inherently complex and multi-causal. So even if it is case that a given process, accurately described in the present, creates a tendency for a certain kind of result -- it remains the case that there may well be other processes at work that will offset this result. The tendency of powerful agents to seize opportunities for enhancing their wealth through processes of urban development implies a certain kind of urban geography in the future; but this outcome might be offset by a genuinely robust and sustained citizens' movement at the city council level.

The idea that historical predictions are generally probabilistic is partly a consequence of the fact of the existence of unknown ceteris paribus conditions. But it is also, more fundamentally, a consequence of the fact that social causation itself is almost always probabilistic. If we say that rising conflict over important resources (X) is a cause of inter-group violence (Y), we don't mean that X is necessarily followed by Y; instead, we mean that X raises the likelihood of the occurrence of Y.

So two conclusions seem justified. First, there is a perfectly valid intellectual role for making historical predictions. But these need to be modest predictions: limited in scope, closely tied to theories of existing social mechanisms, and accompanied by ceteris paribus conditions. And second, grand predictions should be treated with great suspicion. At their best, they depend on identifying a few existing mechanisms and processes; but the fact of multi-causal historical change, the fact of the compounding of uncertainties, and the fact of the unpredictability of complex systems should all make us dubious about large and immodest claims about the future. For the big answers, we really have to wait for the owl of Minerva to spread her wings.