Policy, treatment, and mechanism

Policies are selected in order to bring about some desired social outcome or to prevent an undesired one. Medical treatments are applied in order to cure a disease or to ameliorate its effects. In each case an intervention is performed in the belief that this intervention will causally interact with a larger system in such a way as to bring about the desired state. On the basis of a body of beliefs and theories, we judge that T in circumstances C will bring about O with some degree of likelihood. If we did not have such a belief, then there would be no rational basis for choosing to apply the treatment. "Try something, try anything" isn't exactly a rational basis for policy choice.

In other words, policies and treatments depend on the availability of bodies of knowledge about the causal structure of the domain we're interested in -- what sorts of factors cause or inhibit what sorts of outcomes. This means we need to have some knowledge of the mechanisms that are at work in this domain. And it also means that we need to have some degree of ability to predict some future states -- "If you give the patient an aspirin her fever will come down" or "If we inject $700 billion into the financial system the stock market will recover."

Predictions of this sort could be grounded in two different sorts of reasoning. They might be purely inductive: "Clinical studies demonstrate that administration of an aspirin has a 90% probability of reducing fever." Or they could be based on hypotheses about the mechanisms that are operative: "Fever is caused by C; aspirin reduces C in the bloodstream; therefore we should expect that aspirin reduces fever by reducing C." And ideally we would hope that both forms of reasoning are available -- causal expectations are born out by clinical evidence.

Implicitly this story assumes that the relevant causal systems are pretty simple -- that there are only a few causal pathways and that it is possible to isolate them through experimental studies. We can then insert our proposed interventions into the causal diagram and have reasonable confidence that we can anticipate their effects. The logic of clinical trials as a way of establishing efficacy depends on this assumption of causal simplicity and isolation.

But what if the domain we're concerned with isn't like that? Suppose instead that there are many causal factors and a high degree of causal interdependence among the factors. And suppose that we have only limited knowledge of the strength and form of these interdependencies. Is it possible to make rationally justified interventions within such a system?

This description comes pretty close to what are referred to as complex systems. And the most basic finding in the study of complex systems is the extreme difficulty of anticipating future system states. Small interventions or variations in boundary conditions produce massive variations in later system states. But this is bad news for policy makers who are hoping to "steer" a complex system towards a more desirable state. There are good analytical reasons for thinking that they will not be able to anticipate the nature or magnitude or even direction of the effects of the intervention.

The study of complex systems is a collection of areas of research in mathematics, economics, and biology that attempt to arrive at better ways of modeling and projecting the behavior of systems with these complex causal interdependencies. This is an exciting field of research at places like the Santa Fe Institute and the University of Michigan. One important tool that had been extensively developed is the theory of agent-based modeling -- essentially, the effort to derive system properties as the aggregate result of the activities of independent agents at the micro-level. And a fairly durable result has emerged: run a model of a complex system a thousand times and you will get a wide distribution of outcomes. This means that we need to think of complex systems as being highly contingent and path-dependent in their behavior. The effect of an intervention may be a wide distribution of future states.

So far the argument is located at a pretty high level of abstraction. Simple causal systems admit of intelligent policy intervention, whereas complex, chaotic systems may not. But the important question is more concrete: which kind of system are we facing when we consider social policy or disease? Are social systems and diseases examples of complex systems? Can social systems be sufficiently disaggregated into fairly durable subsystems that admit of discrete causal analysis and intelligent intervention? What about diseases such as solid tumors? Can we have confidence in interventions such as chemotherapy? And, in both realms, can the findings of complexity theory be helpful by providing mathematical means for working out the system effects of various possible interventions?

Policy, treatment, and mechanism

Policies are selected in order to bring about some desired social outcome or to prevent an undesired one. Medical treatments are applied in order to cure a disease or to ameliorate its effects. In each case an intervention is performed in the belief that this intervention will causally interact with a larger system in such a way as to bring about the desired state. On the basis of a body of beliefs and theories, we judge that T in circumstances C will bring about O with some degree of likelihood. If we did not have such a belief, then there would be no rational basis for choosing to apply the treatment. "Try something, try anything" isn't exactly a rational basis for policy choice.

In other words, policies and treatments depend on the availability of bodies of knowledge about the causal structure of the domain we're interested in -- what sorts of factors cause or inhibit what sorts of outcomes. This means we need to have some knowledge of the mechanisms that are at work in this domain. And it also means that we need to have some degree of ability to predict some future states -- "If you give the patient an aspirin her fever will come down" or "If we inject $700 billion into the financial system the stock market will recover."

Predictions of this sort could be grounded in two different sorts of reasoning. They might be purely inductive: "Clinical studies demonstrate that administration of an aspirin has a 90% probability of reducing fever." Or they could be based on hypotheses about the mechanisms that are operative: "Fever is caused by C; aspirin reduces C in the bloodstream; therefore we should expect that aspirin reduces fever by reducing C." And ideally we would hope that both forms of reasoning are available -- causal expectations are born out by clinical evidence.

Implicitly this story assumes that the relevant causal systems are pretty simple -- that there are only a few causal pathways and that it is possible to isolate them through experimental studies. We can then insert our proposed interventions into the causal diagram and have reasonable confidence that we can anticipate their effects. The logic of clinical trials as a way of establishing efficacy depends on this assumption of causal simplicity and isolation.

But what if the domain we're concerned with isn't like that? Suppose instead that there are many causal factors and a high degree of causal interdependence among the factors. And suppose that we have only limited knowledge of the strength and form of these interdependencies. Is it possible to make rationally justified interventions within such a system?

This description comes pretty close to what are referred to as complex systems. And the most basic finding in the study of complex systems is the extreme difficulty of anticipating future system states. Small interventions or variations in boundary conditions produce massive variations in later system states. But this is bad news for policy makers who are hoping to "steer" a complex system towards a more desirable state. There are good analytical reasons for thinking that they will not be able to anticipate the nature or magnitude or even direction of the effects of the intervention.

The study of complex systems is a collection of areas of research in mathematics, economics, and biology that attempt to arrive at better ways of modeling and projecting the behavior of systems with these complex causal interdependencies. This is an exciting field of research at places like the Santa Fe Institute and the University of Michigan. One important tool that had been extensively developed is the theory of agent-based modeling -- essentially, the effort to derive system properties as the aggregate result of the activities of independent agents at the micro-level. And a fairly durable result has emerged: run a model of a complex system a thousand times and you will get a wide distribution of outcomes. This means that we need to think of complex systems as being highly contingent and path-dependent in their behavior. The effect of an intervention may be a wide distribution of future states.

So far the argument is located at a pretty high level of abstraction. Simple causal systems admit of intelligent policy intervention, whereas complex, chaotic systems may not. But the important question is more concrete: which kind of system are we facing when we consider social policy or disease? Are social systems and diseases examples of complex systems? Can social systems be sufficiently disaggregated into fairly durable subsystems that admit of discrete causal analysis and intelligent intervention? What about diseases such as solid tumors? Can we have confidence in interventions such as chemotherapy? And, in both realms, can the findings of complexity theory be helpful by providing mathematical means for working out the system effects of various possible interventions?

Policy, treatment, and mechanism

Policies are selected in order to bring about some desired social outcome or to prevent an undesired one. Medical treatments are applied in order to cure a disease or to ameliorate its effects. In each case an intervention is performed in the belief that this intervention will causally interact with a larger system in such a way as to bring about the desired state. On the basis of a body of beliefs and theories, we judge that T in circumstances C will bring about O with some degree of likelihood. If we did not have such a belief, then there would be no rational basis for choosing to apply the treatment. "Try something, try anything" isn't exactly a rational basis for policy choice.

In other words, policies and treatments depend on the availability of bodies of knowledge about the causal structure of the domain we're interested in -- what sorts of factors cause or inhibit what sorts of outcomes. This means we need to have some knowledge of the mechanisms that are at work in this domain. And it also means that we need to have some degree of ability to predict some future states -- "If you give the patient an aspirin her fever will come down" or "If we inject $700 billion into the financial system the stock market will recover."

Predictions of this sort could be grounded in two different sorts of reasoning. They might be purely inductive: "Clinical studies demonstrate that administration of an aspirin has a 90% probability of reducing fever." Or they could be based on hypotheses about the mechanisms that are operative: "Fever is caused by C; aspirin reduces C in the bloodstream; therefore we should expect that aspirin reduces fever by reducing C." And ideally we would hope that both forms of reasoning are available -- causal expectations are born out by clinical evidence.

Implicitly this story assumes that the relevant causal systems are pretty simple -- that there are only a few causal pathways and that it is possible to isolate them through experimental studies. We can then insert our proposed interventions into the causal diagram and have reasonable confidence that we can anticipate their effects. The logic of clinical trials as a way of establishing efficacy depends on this assumption of causal simplicity and isolation.

But what if the domain we're concerned with isn't like that? Suppose instead that there are many causal factors and a high degree of causal interdependence among the factors. And suppose that we have only limited knowledge of the strength and form of these interdependencies. Is it possible to make rationally justified interventions within such a system?

This description comes pretty close to what are referred to as complex systems. And the most basic finding in the study of complex systems is the extreme difficulty of anticipating future system states. Small interventions or variations in boundary conditions produce massive variations in later system states. But this is bad news for policy makers who are hoping to "steer" a complex system towards a more desirable state. There are good analytical reasons for thinking that they will not be able to anticipate the nature or magnitude or even direction of the effects of the intervention.

The study of complex systems is a collection of areas of research in mathematics, economics, and biology that attempt to arrive at better ways of modeling and projecting the behavior of systems with these complex causal interdependencies. This is an exciting field of research at places like the Santa Fe Institute and the University of Michigan. One important tool that had been extensively developed is the theory of agent-based modeling -- essentially, the effort to derive system properties as the aggregate result of the activities of independent agents at the micro-level. And a fairly durable result has emerged: run a model of a complex system a thousand times and you will get a wide distribution of outcomes. This means that we need to think of complex systems as being highly contingent and path-dependent in their behavior. The effect of an intervention may be a wide distribution of future states.

So far the argument is located at a pretty high level of abstraction. Simple causal systems admit of intelligent policy intervention, whereas complex, chaotic systems may not. But the important question is more concrete: which kind of system are we facing when we consider social policy or disease? Are social systems and diseases examples of complex systems? Can social systems be sufficiently disaggregated into fairly durable subsystems that admit of discrete causal analysis and intelligent intervention? What about diseases such as solid tumors? Can we have confidence in interventions such as chemotherapy? And, in both realms, can the findings of complexity theory be helpful by providing mathematical means for working out the system effects of various possible interventions?

What to do?

Decision makers at every level are perplexed by the turbulence created by the current financial crisis. Everyone is acting under great uncertainty -- business owners, state governors, the Department of the Treasury, the presidential candidates, and university officials. And yet today's actions may have enormous effects on the business, the non-profit organization, the family, or the state tomorrow. Liquidity drought, variations in cash flow, the availability of credit for major capital projects, the possibility of bankruptcy of major and essential business partners such as contractors, the possibility of further major job losses in various regions, abrupt decline in demand for houses and cars, and a plummeting stock market make for an environment of choice in which every action can have seriously bad consequences.

I'm thinking here particularly of decision makers who are responsible for large, complex organizations -- organizations that deliver services or products, that depend on staff and facilities to fulfill their mission, that need to make medium-term plans about investments of resources for future use, and that depend on assumptions about revenues in the future to satisfy the cash-flow needs of the enterprise to stay financially viable. What is noteworthy about this situation of choice is that it necessarily involves plans and projections about the future. And it involves the temporally extended coordination of activities of people and expenditures of resources over a prolonged period of time. This means that it isn't possible to achieve success through a purely opportunistic and moment-by-moment response to events. You can't fly a complex organization by the seat of your pants.

So how do we plan for the best outcomes in the current circumstances of turbulence and uncertainty? The normal assumption of continuity -- assume that most circumstances won't change much between today and tomorrow -- is distinctly inappropriate. Today's economic and financial environment has many moving parts -- and they interact in surprising ways. And unfortunately, it appears that there is no reliable science to guide us here. Experts disagree about the mechanisms and the potential remedies of the current crisis. Navigating this environment is akin to trying to catch a pingpong ball in a crowded wind tunnel.

So what is to be done? I mean to pose this question in the most pragmatic way possible. Given deep uncertainty about the changes that may occur tomorrow, and given a fairly deep ignorance of the fundamental mechanisms that are affecting this storm, how can prudent stewards of institutions and businesses do the best job possible to preserve the fundamental interests of their stakeholders and carry out the missions of their organizations?

There are a couple of rational-choice answers to this question, falling generally under the topic of "decision making under risk and uncertainty." One prescription is the expected utility rule: identify the possible actions and their various outcomes; assign a probability and utility to each outcome; and choose the action with the greatest expected utility (sum of the products of probability and utility for the outcomes). Or we might consider the more risk-averse rule, the minimax principle: choose that action with the least bad worst possible outcome.

Unfortunately, neither of these decision procedures is of much help in current circumstances. We don't know what the range of possible outcomes is for a given action or policy (because we don't understand the mechanisms); we can't assign likelihoods to outcomes; and it's not feasible to measure the utility of a given outcome, all things considered. So expected utility decision-making doesn't seem very helpful in current circumstances.

Another approach depends on what we might call "locally rational decision-making" or heuristic decision-making. We don't know what the consequences of an extended liquidity crisis are, but we may reason that more reserves are always useful; so we may choose to curtail spending today to increase reserves. Or we may observe that an additional debt burden for the business is likely to be a handicap in the future, so we may postpone important capital projects. Or we may defer hiring additional staff in order to preserve more budget flexibility in the future. But notice that there are rules of thumb that point in the opposite direction: "A business should take bold moves in times of crisis, to leave it in a stronger position when recovery begins." This advice would encourage investment rather than curtailing it. So the problem with rules of thumb is that they often come into conflict with each other -- leaving the decision maker in a quandary.

In fact, if the range of uncertainty is great enough, it is impossible to be prudent. Prudence involves taking steps today that are most likely to leave you in a satisfactory position tomorrow. But if we are uncertain about even the most basic truisms -- if we are in a position where the most basic assumptions of continuity are defeated -- then we can't begin to weigh possible outcomes or design prudent strategies. It's hard to see how a pilot could fly "prudently" in the dark and without instruments.

It would seem that today's financial crisis doesn't create quite this degree of radical uncertainty, however. It is a fair bet that the world economy and financial system will survive and will recover within a few years. It is likely enough that cash reserves will be very useful for organizations and businesses in the next few years. It is likely that risky decisions have a greater potential for enterprise ruin than in normal times -- "high risk, high reward" is probably a bad rule to follow in current circumstances. And these assumptions add up to a counsel of conservation of resources, flexibility of activities, cost discipline, and preservation of accessible reserves.

What to do?

Decision makers at every level are perplexed by the turbulence created by the current financial crisis. Everyone is acting under great uncertainty -- business owners, state governors, the Department of the Treasury, the presidential candidates, and university officials. And yet today's actions may have enormous effects on the business, the non-profit organization, the family, or the state tomorrow. Liquidity drought, variations in cash flow, the availability of credit for major capital projects, the possibility of bankruptcy of major and essential business partners such as contractors, the possibility of further major job losses in various regions, abrupt decline in demand for houses and cars, and a plummeting stock market make for an environment of choice in which every action can have seriously bad consequences.

I'm thinking here particularly of decision makers who are responsible for large, complex organizations -- organizations that deliver services or products, that depend on staff and facilities to fulfill their mission, that need to make medium-term plans about investments of resources for future use, and that depend on assumptions about revenues in the future to satisfy the cash-flow needs of the enterprise to stay financially viable. What is noteworthy about this situation of choice is that it necessarily involves plans and projections about the future. And it involves the temporally extended coordination of activities of people and expenditures of resources over a prolonged period of time. This means that it isn't possible to achieve success through a purely opportunistic and moment-by-moment response to events. You can't fly a complex organization by the seat of your pants.

So how do we plan for the best outcomes in the current circumstances of turbulence and uncertainty? The normal assumption of continuity -- assume that most circumstances won't change much between today and tomorrow -- is distinctly inappropriate. Today's economic and financial environment has many moving parts -- and they interact in surprising ways. And unfortunately, it appears that there is no reliable science to guide us here. Experts disagree about the mechanisms and the potential remedies of the current crisis. Navigating this environment is akin to trying to catch a pingpong ball in a crowded wind tunnel.

So what is to be done? I mean to pose this question in the most pragmatic way possible. Given deep uncertainty about the changes that may occur tomorrow, and given a fairly deep ignorance of the fundamental mechanisms that are affecting this storm, how can prudent stewards of institutions and businesses do the best job possible to preserve the fundamental interests of their stakeholders and carry out the missions of their organizations?

There are a couple of rational-choice answers to this question, falling generally under the topic of "decision making under risk and uncertainty." One prescription is the expected utility rule: identify the possible actions and their various outcomes; assign a probability and utility to each outcome; and choose the action with the greatest expected utility (sum of the products of probability and utility for the outcomes). Or we might consider the more risk-averse rule, the minimax principle: choose that action with the least bad worst possible outcome.

Unfortunately, neither of these decision procedures is of much help in current circumstances. We don't know what the range of possible outcomes is for a given action or policy (because we don't understand the mechanisms); we can't assign likelihoods to outcomes; and it's not feasible to measure the utility of a given outcome, all things considered. So expected utility decision-making doesn't seem very helpful in current circumstances.

Another approach depends on what we might call "locally rational decision-making" or heuristic decision-making. We don't know what the consequences of an extended liquidity crisis are, but we may reason that more reserves are always useful; so we may choose to curtail spending today to increase reserves. Or we may observe that an additional debt burden for the business is likely to be a handicap in the future, so we may postpone important capital projects. Or we may defer hiring additional staff in order to preserve more budget flexibility in the future. But notice that there are rules of thumb that point in the opposite direction: "A business should take bold moves in times of crisis, to leave it in a stronger position when recovery begins." This advice would encourage investment rather than curtailing it. So the problem with rules of thumb is that they often come into conflict with each other -- leaving the decision maker in a quandary.

In fact, if the range of uncertainty is great enough, it is impossible to be prudent. Prudence involves taking steps today that are most likely to leave you in a satisfactory position tomorrow. But if we are uncertain about even the most basic truisms -- if we are in a position where the most basic assumptions of continuity are defeated -- then we can't begin to weigh possible outcomes or design prudent strategies. It's hard to see how a pilot could fly "prudently" in the dark and without instruments.

It would seem that today's financial crisis doesn't create quite this degree of radical uncertainty, however. It is a fair bet that the world economy and financial system will survive and will recover within a few years. It is likely enough that cash reserves will be very useful for organizations and businesses in the next few years. It is likely that risky decisions have a greater potential for enterprise ruin than in normal times -- "high risk, high reward" is probably a bad rule to follow in current circumstances. And these assumptions add up to a counsel of conservation of resources, flexibility of activities, cost discipline, and preservation of accessible reserves.

What to do?

Decision makers at every level are perplexed by the turbulence created by the current financial crisis. Everyone is acting under great uncertainty -- business owners, state governors, the Department of the Treasury, the presidential candidates, and university officials. And yet today's actions may have enormous effects on the business, the non-profit organization, the family, or the state tomorrow. Liquidity drought, variations in cash flow, the availability of credit for major capital projects, the possibility of bankruptcy of major and essential business partners such as contractors, the possibility of further major job losses in various regions, abrupt decline in demand for houses and cars, and a plummeting stock market make for an environment of choice in which every action can have seriously bad consequences.

I'm thinking here particularly of decision makers who are responsible for large, complex organizations -- organizations that deliver services or products, that depend on staff and facilities to fulfill their mission, that need to make medium-term plans about investments of resources for future use, and that depend on assumptions about revenues in the future to satisfy the cash-flow needs of the enterprise to stay financially viable. What is noteworthy about this situation of choice is that it necessarily involves plans and projections about the future. And it involves the temporally extended coordination of activities of people and expenditures of resources over a prolonged period of time. This means that it isn't possible to achieve success through a purely opportunistic and moment-by-moment response to events. You can't fly a complex organization by the seat of your pants.

So how do we plan for the best outcomes in the current circumstances of turbulence and uncertainty? The normal assumption of continuity -- assume that most circumstances won't change much between today and tomorrow -- is distinctly inappropriate. Today's economic and financial environment has many moving parts -- and they interact in surprising ways. And unfortunately, it appears that there is no reliable science to guide us here. Experts disagree about the mechanisms and the potential remedies of the current crisis. Navigating this environment is akin to trying to catch a pingpong ball in a crowded wind tunnel.

So what is to be done? I mean to pose this question in the most pragmatic way possible. Given deep uncertainty about the changes that may occur tomorrow, and given a fairly deep ignorance of the fundamental mechanisms that are affecting this storm, how can prudent stewards of institutions and businesses do the best job possible to preserve the fundamental interests of their stakeholders and carry out the missions of their organizations?

There are a couple of rational-choice answers to this question, falling generally under the topic of "decision making under risk and uncertainty." One prescription is the expected utility rule: identify the possible actions and their various outcomes; assign a probability and utility to each outcome; and choose the action with the greatest expected utility (sum of the products of probability and utility for the outcomes). Or we might consider the more risk-averse rule, the minimax principle: choose that action with the least bad worst possible outcome.

Unfortunately, neither of these decision procedures is of much help in current circumstances. We don't know what the range of possible outcomes is for a given action or policy (because we don't understand the mechanisms); we can't assign likelihoods to outcomes; and it's not feasible to measure the utility of a given outcome, all things considered. So expected utility decision-making doesn't seem very helpful in current circumstances.

Another approach depends on what we might call "locally rational decision-making" or heuristic decision-making. We don't know what the consequences of an extended liquidity crisis are, but we may reason that more reserves are always useful; so we may choose to curtail spending today to increase reserves. Or we may observe that an additional debt burden for the business is likely to be a handicap in the future, so we may postpone important capital projects. Or we may defer hiring additional staff in order to preserve more budget flexibility in the future. But notice that there are rules of thumb that point in the opposite direction: "A business should take bold moves in times of crisis, to leave it in a stronger position when recovery begins." This advice would encourage investment rather than curtailing it. So the problem with rules of thumb is that they often come into conflict with each other -- leaving the decision maker in a quandary.

In fact, if the range of uncertainty is great enough, it is impossible to be prudent. Prudence involves taking steps today that are most likely to leave you in a satisfactory position tomorrow. But if we are uncertain about even the most basic truisms -- if we are in a position where the most basic assumptions of continuity are defeated -- then we can't begin to weigh possible outcomes or design prudent strategies. It's hard to see how a pilot could fly "prudently" in the dark and without instruments.

It would seem that today's financial crisis doesn't create quite this degree of radical uncertainty, however. It is a fair bet that the world economy and financial system will survive and will recover within a few years. It is likely enough that cash reserves will be very useful for organizations and businesses in the next few years. It is likely that risky decisions have a greater potential for enterprise ruin than in normal times -- "high risk, high reward" is probably a bad rule to follow in current circumstances. And these assumptions add up to a counsel of conservation of resources, flexibility of activities, cost discipline, and preservation of accessible reserves.

Strange parallels

Victor Lieberman uses the phrase, "strange parallels," as the title for his two-volume study of Southeast Asian history (Strange Parallels: Volume 1, Integration on the Mainland: Southeast Asia in Global Context, c. 800-1830). Besides offering a highly expert history of Burma and its many kingdoms between 800 and 1830, Lieberman poses a fascinating and novel question: how can we explain the substantial historical parallels that existed between Burma and various parts of Europe, including especially France and Russia? He writes:

In fact, in mainland Southeast Asia as well as in France, the late 18th and early 19th centuries ended the third and inaugurated the last of four roughly synchronized cycles of political consolidation that together spanned the better part of a millenium. (2)

fig. 1.2. Territorial consolidation (p. 4)

The figure illustrates the kind of synchrony that Lieberman is highlighting -- over a sweep of some thousand years, there is a rough-and-ready correspondence in the patterns of territorial consolidation that existed in Burma and France.

Lieberman poses the crucial historical question in these terms: "Why should distant regions, with no obvious religious or material links, have experienced more or less coordinated cycles? If we discount coincidence, what hitherto invisible ties could have spanned the continents?" (2)

Apriori there appear to be only a small number of logically distinct possibilities that could explain the fact that patterns of variation are synchronized between A and B. (1) It may be that there is a causal linkage from A to B such that A's variations stimulate subsequent variations in B. (2) It may be that there is a common cause C whose variation simultaneously causes variation in both A and B. Global climate variation might be such an example. (3) It may be that both A and B are running through their variations according to the same internal clock, with the result that their variations are synchronized without causal interaction -- like two clocks ringing out the hours in tandem or two adolescent children going through roughly the same developmental stages at roughly the same time. (4) It may be that the observed synchrony is simple coincidence, with no causal explanation whatsoever.

So far as I can see, this exhausts the logical possibilities; there is no fifth possibility.

To further complicate the picture, Lieberman points out that there were other regions of the world where these patterns of consolidation did not occur, or did so on a very different timeline. So we can exclude the idea that there was some common global cause leading to simultaneous pulses of consolidation; rather, Southeast Asia and Western Europe were synchronized, but India was not.

Lieberman's explanation of this observed historical synchrony goes along these lines. He believes that both internalist and externalist approaches have a role to play. The internal historical dynamics of the state systems in Burma and Western Europe were governed by particular local factors. But they each created a tendency towards consolidation of land and power. And external factors provided periodic "pulses" that served to synchronize these internal patterns of development. So the effects of an external factor -- maritime trade -- pushed both Western Europe and Burma into extended periods of state formation and consolidation. This story combines (3) and (2) above: local processes that are developing according to their own imperatives, and occasional system-wide pulses that bring these local processes into synchrony. And the explanation allows Lieberman to place the intellectual frameworks of both Tilly and Wallerstein into the story.

Lieberman's approach is important for the philosophy of history because it leads us to ask different questions about historical causation and historical time. And it provides important new thinking about how to approach the nexus between regional history and global history.