Consolidated quantitative history

It is fascinating to browse through the sessions on the program at the Social Science History Association this month (link). SSHA is distinguished by its deep embrace of disciplinary and methodological diversity, and there are panels deriving from qualitative, comparative, and theoretical perspectives. But particularly interesting for me this year are the more quantitative subjects -- reflecting the cliometric impulse that led to the formation of the SSHA several decades ago.  (Here are a few comments by Julia Adams, Elisabeth Stephanie Clemens, and Anne Shola Orloff, past and current presidents of SSHA, on this history.) There are panels on historical measures of the standard of living in different parts of Eurasia; on fertility, mobility, and population size in small and large regions; on longterm climate and atmospheric fluctuation over time (the year without a summer in mid-nineteenth century); levels of agricultural productivity over several centuries in several regions; the degree of inequality in landholding in Scania and North China; and many other fascinating studies of measurable social properties. And, of course, the papers offer time graphs of the variables that are the subject of the study.

So what if we had a goal of providing a unified and public measurement of factors like these over a large expanse of time and space? What if we set out to synthesize many studies currently underway and arrive at a common set of measures over time for these regions?

To an extent this is the goal of the Eurasian Population and Family History Project: to assemble a large set of research groups across Eurasia, measuring demographic data using comparable methods in the several locations (link). Though the project hasn't yet produced a synthetic volume summarizing all the results, we can hope that this kind of product will eventually be forthcoming. The researchers describe the project in these terms: “New data and new methods … have begun to illuminate the complexities of demographic responses to exogenous stress, economic and otherwise.… Combined time-series and event-history analyses of longitudinal, nominative, microlevel data now allow for the finely grained differentiation of mortality, fertility, and other demographic responses by social class, household context, and other dimensions at the individual level” (Tommy Bengtsson, Cameron Campbell, James Z. Lee, et al, Life under Pressure: Mortality and Living Standards in Europe and Asia, 1700-1900 (Eurasian Population and Family History), 2004, pp. viii-ix). Their goal is an ambitious one; it is to provide detailed, analytically sophisticated multi-generational studies of a number of populations across Eurasia. The studies are intended to permit the researchers to probe issues of causation as well as to identify important dimensions of similarity and difference across regions and communities.  The most recent volume in the series appeared earlier this fall (Noriko Tsuya, Wang Feng, George Alter, James Z. Lee, et al, Prudence and Pressure: Reproduction and Human Agency in Europe and Asia, 1700-1900 (Eurasian Population and Family History)).

Suppose we wanted to go further and create an interactive Wiki site that permitted researchers to upload their findings for a specified set of variables; and suppose the underlying software created a dynamic set of time graphs and maps representing these data over time. And suppose that the data displays can be broken out at different levels of scale -- North China, China, Eurasia. Finally, of course, we would want to specify that the data summaries are tagged with meta-data indicating the studies and methodologies leading to the graph. Could we say that this hypothetical site would then represent the meta-knowledge of the community of economic historians, climate scientists, and historical demographers? And could we speculate that this product would be an enormous benefit for historical researchers in a broad range of disciplines?

We can immediately predict some limitations to such a collective project. Most important is the unavoidable incompleteness of the data. We may have studies on farm productivity that document output for portions of North China and portions of the Yangzi Delta. But, of course, this doesn't tell us much about western China. So we can't realistically aspire to a full and complete representation of the variables full regions and periods.

Second, there is the problem of methodological inconsistencies across studies. Robert Allen is a leader in attempting to document standard of living across Europe and Asia (Robert Allen, ed., Living Standards in the Past: New Perspectives on Well-Being in Asia and Europe). And a central problem he faces is that multiple studies estimate consumption and wellbeing in different ways. So forming a composite representation requires an additional set of assumptions and models by the meta-study researcher.

Third, there is the question of defining the role for verbal analysis and reasoning in such a knowledge system. Are we to imagine this collective data set as a universal data appendix to a huge range of verbal historical narratives and analyses? Or might we come to think that the graphs speak for themselves, with no need for verbal analysis and inference?

All that said, I think the hypothetical Wiki site would be enormously valuable. It would provide us with birds-eye view of the large structural and material features that defined and constrained Eurasian history. And it has the potential of suggesting new avenues of research and new causal hypotheses about documented processes of change. For example, we may compare the time series of life expectancy and average temperature, and we may hypothesize that mortality and fertility were affected by abnormal climate conditions (through the medium of agricultural performance). But we may also be able to observe suggestive correlations between material variables and behavior -- for example, between ecological crises and the frequency of peasant uprisings. Or, conceivably, our eye might be led to a graph of sex ratios in a region and another of the incidence of banditry, and we might be led to a "bare sticks" hypothesis about social unrest: when there is an excess of unmarried young men, we can expect an upsurge of banditry and crime.

There are increasingly powerful tools available that permit scholars and the interested public to explore large public datasets such as the US Census or Bureau of Labor Statistics (link).  It is perhaps not wholly unrealistic to imagine a platform that permits multiple researchers to contribute to a meta-dataset for economic and social history of the world.

Consolidated quantitative history

It is fascinating to browse through the sessions on the program at the Social Science History Association this month (link). SSHA is distinguished by its deep embrace of disciplinary and methodological diversity, and there are panels deriving from qualitative, comparative, and theoretical perspectives. But particularly interesting for me this year are the more quantitative subjects -- reflecting the cliometric impulse that led to the formation of the SSHA several decades ago.  (Here are a few comments by Julia Adams, Elisabeth Stephanie Clemens, and Anne Shola Orloff, past and current presidents of SSHA, on this history.) There are panels on historical measures of the standard of living in different parts of Eurasia; on fertility, mobility, and population size in small and large regions; on longterm climate and atmospheric fluctuation over time (the year without a summer in mid-nineteenth century); levels of agricultural productivity over several centuries in several regions; the degree of inequality in landholding in Scania and North China; and many other fascinating studies of measurable social properties. And, of course, the papers offer time graphs of the variables that are the subject of the study.

So what if we had a goal of providing a unified and public measurement of factors like these over a large expanse of time and space? What if we set out to synthesize many studies currently underway and arrive at a common set of measures over time for these regions?

To an extent this is the goal of the Eurasian Population and Family History Project: to assemble a large set of research groups across Eurasia, measuring demographic data using comparable methods in the several locations (link). Though the project hasn't yet produced a synthetic volume summarizing all the results, we can hope that this kind of product will eventually be forthcoming. The researchers describe the project in these terms: “New data and new methods … have begun to illuminate the complexities of demographic responses to exogenous stress, economic and otherwise.… Combined time-series and event-history analyses of longitudinal, nominative, microlevel data now allow for the finely grained differentiation of mortality, fertility, and other demographic responses by social class, household context, and other dimensions at the individual level” (Tommy Bengtsson, Cameron Campbell, James Z. Lee, et al, Life under Pressure: Mortality and Living Standards in Europe and Asia, 1700-1900 (Eurasian Population and Family History), 2004, pp. viii-ix). Their goal is an ambitious one; it is to provide detailed, analytically sophisticated multi-generational studies of a number of populations across Eurasia. The studies are intended to permit the researchers to probe issues of causation as well as to identify important dimensions of similarity and difference across regions and communities.  The most recent volume in the series appeared earlier this fall (Noriko Tsuya, Wang Feng, George Alter, James Z. Lee, et al, Prudence and Pressure: Reproduction and Human Agency in Europe and Asia, 1700-1900 (Eurasian Population and Family History)).

Suppose we wanted to go further and create an interactive Wiki site that permitted researchers to upload their findings for a specified set of variables; and suppose the underlying software created a dynamic set of time graphs and maps representing these data over time. And suppose that the data displays can be broken out at different levels of scale -- North China, China, Eurasia. Finally, of course, we would want to specify that the data summaries are tagged with meta-data indicating the studies and methodologies leading to the graph. Could we say that this hypothetical site would then represent the meta-knowledge of the community of economic historians, climate scientists, and historical demographers? And could we speculate that this product would be an enormous benefit for historical researchers in a broad range of disciplines?

We can immediately predict some limitations to such a collective project. Most important is the unavoidable incompleteness of the data. We may have studies on farm productivity that document output for portions of North China and portions of the Yangzi Delta. But, of course, this doesn't tell us much about western China. So we can't realistically aspire to a full and complete representation of the variables full regions and periods.

Second, there is the problem of methodological inconsistencies across studies. Robert Allen is a leader in attempting to document standard of living across Europe and Asia (Robert Allen, ed., Living Standards in the Past: New Perspectives on Well-Being in Asia and Europe). And a central problem he faces is that multiple studies estimate consumption and wellbeing in different ways. So forming a composite representation requires an additional set of assumptions and models by the meta-study researcher.

Third, there is the question of defining the role for verbal analysis and reasoning in such a knowledge system. Are we to imagine this collective data set as a universal data appendix to a huge range of verbal historical narratives and analyses? Or might we come to think that the graphs speak for themselves, with no need for verbal analysis and inference?

All that said, I think the hypothetical Wiki site would be enormously valuable. It would provide us with birds-eye view of the large structural and material features that defined and constrained Eurasian history. And it has the potential of suggesting new avenues of research and new causal hypotheses about documented processes of change. For example, we may compare the time series of life expectancy and average temperature, and we may hypothesize that mortality and fertility were affected by abnormal climate conditions (through the medium of agricultural performance). But we may also be able to observe suggestive correlations between material variables and behavior -- for example, between ecological crises and the frequency of peasant uprisings. Or, conceivably, our eye might be led to a graph of sex ratios in a region and another of the incidence of banditry, and we might be led to a "bare sticks" hypothesis about social unrest: when there is an excess of unmarried young men, we can expect an upsurge of banditry and crime.

There are increasingly powerful tools available that permit scholars and the interested public to explore large public datasets such as the US Census or Bureau of Labor Statistics (link).  It is perhaps not wholly unrealistic to imagine a platform that permits multiple researchers to contribute to a meta-dataset for economic and social history of the world.

Consolidated quantitative history

It is fascinating to browse through the sessions on the program at the Social Science History Association this month (link). SSHA is distinguished by its deep embrace of disciplinary and methodological diversity, and there are panels deriving from qualitative, comparative, and theoretical perspectives. But particularly interesting for me this year are the more quantitative subjects -- reflecting the cliometric impulse that led to the formation of the SSHA several decades ago.  (Here are a few comments by Julia Adams, Elisabeth Stephanie Clemens, and Anne Shola Orloff, past and current presidents of SSHA, on this history.) There are panels on historical measures of the standard of living in different parts of Eurasia; on fertility, mobility, and population size in small and large regions; on longterm climate and atmospheric fluctuation over time (the year without a summer in mid-nineteenth century); levels of agricultural productivity over several centuries in several regions; the degree of inequality in landholding in Scania and North China; and many other fascinating studies of measurable social properties. And, of course, the papers offer time graphs of the variables that are the subject of the study.

So what if we had a goal of providing a unified and public measurement of factors like these over a large expanse of time and space? What if we set out to synthesize many studies currently underway and arrive at a common set of measures over time for these regions?

To an extent this is the goal of the Eurasian Population and Family History Project: to assemble a large set of research groups across Eurasia, measuring demographic data using comparable methods in the several locations (link). Though the project hasn't yet produced a synthetic volume summarizing all the results, we can hope that this kind of product will eventually be forthcoming. The researchers describe the project in these terms: “New data and new methods … have begun to illuminate the complexities of demographic responses to exogenous stress, economic and otherwise.… Combined time-series and event-history analyses of longitudinal, nominative, microlevel data now allow for the finely grained differentiation of mortality, fertility, and other demographic responses by social class, household context, and other dimensions at the individual level” (Tommy Bengtsson, Cameron Campbell, James Z. Lee, et al, Life under Pressure: Mortality and Living Standards in Europe and Asia, 1700-1900 (Eurasian Population and Family History), 2004, pp. viii-ix). Their goal is an ambitious one; it is to provide detailed, analytically sophisticated multi-generational studies of a number of populations across Eurasia. The studies are intended to permit the researchers to probe issues of causation as well as to identify important dimensions of similarity and difference across regions and communities.  The most recent volume in the series appeared earlier this fall (Noriko Tsuya, Wang Feng, George Alter, James Z. Lee, et al, Prudence and Pressure: Reproduction and Human Agency in Europe and Asia, 1700-1900 (Eurasian Population and Family History)).

Suppose we wanted to go further and create an interactive Wiki site that permitted researchers to upload their findings for a specified set of variables; and suppose the underlying software created a dynamic set of time graphs and maps representing these data over time. And suppose that the data displays can be broken out at different levels of scale -- North China, China, Eurasia. Finally, of course, we would want to specify that the data summaries are tagged with meta-data indicating the studies and methodologies leading to the graph. Could we say that this hypothetical site would then represent the meta-knowledge of the community of economic historians, climate scientists, and historical demographers? And could we speculate that this product would be an enormous benefit for historical researchers in a broad range of disciplines?

We can immediately predict some limitations to such a collective project. Most important is the unavoidable incompleteness of the data. We may have studies on farm productivity that document output for portions of North China and portions of the Yangzi Delta. But, of course, this doesn't tell us much about western China. So we can't realistically aspire to a full and complete representation of the variables full regions and periods.

Second, there is the problem of methodological inconsistencies across studies. Robert Allen is a leader in attempting to document standard of living across Europe and Asia (Robert Allen, ed., Living Standards in the Past: New Perspectives on Well-Being in Asia and Europe). And a central problem he faces is that multiple studies estimate consumption and wellbeing in different ways. So forming a composite representation requires an additional set of assumptions and models by the meta-study researcher.

Third, there is the question of defining the role for verbal analysis and reasoning in such a knowledge system. Are we to imagine this collective data set as a universal data appendix to a huge range of verbal historical narratives and analyses? Or might we come to think that the graphs speak for themselves, with no need for verbal analysis and inference?

All that said, I think the hypothetical Wiki site would be enormously valuable. It would provide us with birds-eye view of the large structural and material features that defined and constrained Eurasian history. And it has the potential of suggesting new avenues of research and new causal hypotheses about documented processes of change. For example, we may compare the time series of life expectancy and average temperature, and we may hypothesize that mortality and fertility were affected by abnormal climate conditions (through the medium of agricultural performance). But we may also be able to observe suggestive correlations between material variables and behavior -- for example, between ecological crises and the frequency of peasant uprisings. Or, conceivably, our eye might be led to a graph of sex ratios in a region and another of the incidence of banditry, and we might be led to a "bare sticks" hypothesis about social unrest: when there is an excess of unmarried young men, we can expect an upsurge of banditry and crime.

There are increasingly powerful tools available that permit scholars and the interested public to explore large public datasets such as the US Census or Bureau of Labor Statistics (link).  It is perhaps not wholly unrealistic to imagine a platform that permits multiple researchers to contribute to a meta-dataset for economic and social history of the world.

New modes of historical presentation

Victor Lieberman's Strange Parallels: Volume 1, Integration on the Mainland: Southeast Asia in Global Context, c.800-1830 and Strange Parallels: Volume 2, Mainland Mirrors: Europe, Japan, China, South Asia, and the Islands: Southeast Asia in Global Context, c.800-1830 represent about 1000 pages of careful, dense historical prose extending over two volumes. As previously discussed (link, link), the book reviews a thousand years of history of the polities of France, Kiev, Burma, Japan, and China, it documents a significant correlation of timing across the extremes of Eurasia, and it offers some historical hypotheses about the causes of this synchronicity. It is a long and involved story.

My question here is perhaps a startling one: Is it possible that some alternative modes of presentation would permit the author to represent the heart of the historical findings much more efficiently in the form of a complex animated visual display? Could the empirical heart of the two volumes be summarized in the form of a rich data display over time? Is the verbal narrative simply a clumsy way of representing what really ought to be graphed? What would be gained, and what would be lost by replacing the long complex text with a compact series of graphs and maps?

This thought experiment is possible because Lieberman's argument lends itself to a quantitative interpretation. Essentially he is focusing on factors that can be estimated over time: degree of scope of a regime, degree of integration of institutions, economic productivity, agricultural intensity, rainfall, temperature, population level and density, mortality by disease, and transport capacity, for example. And he is looking for one or more factors whose temporal variations can be interpreted as a causal factor explaining correlations across the graphs. So we could imagine a master graph representing the factual core of the research, with six groups of graphs over time, representing the chief variables for each region over time.  Here is Lieberman's initial effort along these lines, graphing his estimate of "scope and consolidation" of the states of SE Asia and France.  And we can imagine presenting different data series representing his findings about agricultural productivity, mortality, population, climate change, etc., arranged around a single timeline.

We might imagine supplementing these superimposed data series with a series of dated maps representing the territorial scope of the states of the various regions, arranged along a timeline:

 France T1

 France T2

France T3

(A similar series would be constructed for the states of SE Asia.)

What this coordinated series of graphics represents, then, is the core set of facts that Lieberman has synthesized and presented in the book.  By absorbing the social, political, and economic changes represented by this graphical timeline, the reader has gained access for the full set of empirical claims offered by Lieberman.  And, one might say, the presentation is more direct and comprehensible than the verbal description of these changes contained in the text.  Moreover, we might expect that patterns will emerge more or less directly from these graphic presentations -- for example, the synchrony between state crisis and accumulating climate change.

As for what is lost in this version of the story -- several things seem clear. First, much of the narrative that Lieberman provides is synthetic. He attempts to pull together a wide variety of sources in order to arrive at a summary statement such as this: "The Capetian state increased dramatically in scope and administrative competence between 1000 and 1250." So the narrative serves to justify and document a particular inflection point in the long graph of "French polity". It provides the evidentiary basis for the estimate at this period in time.

Second, of course, the packet of graphs I've just described lacks the eloquence and vividness of the prose that Lieberman or other talented historians are able to achieve in telling their stories. It represents only the abstract summary of conclusions, not the nuance of the reasoning or the drama of the story. The prose text is inherently enjoyable to read, and it engages the reader to share the historical puzzle. But, one might argue, the epistemic core of the book is precisely the abstract factual findings, not the prose style. And the reasoning can be captured as a hypertext lying behind the graph -- a sort of annotated hyper-document.

Finally, this notion of arriving at an abstract, schematic representation of a history of something doesn't work at all for many kinds of historical writing. Michael Kammen's Mystic Chords of Memory: The Transformation of Tradition in American Culture is an inherently semiotic argument, working out the ways that public ceremonies and monuments work in the consciousness of a population. Robert Darnton's The Great Cat Massacre: And Other Episodes in French Cultural History is a deft interpretive inquiry, arriving at a complex interpretation of a puzzling set of actions. These examples of great historical writing are evidence-based; but they are not designed to allow estimation of a set of variables over time. And I don't see that there is the possibility of a more abstract and symbolic representation of the historical knowledge they represent.

One might say that what we have encountered here is an important fissure within contemporary historical writing, between "cliometric" research and knowledge (Reflections on the Cliometrics Revolution: Conversations with Economic Historians) and hermeneutic historical knowledge (Paul Ricoeur, Memory, History, Forgetting). The former is primary interested in the processes of change of measurable human or social variables over time, whereas the latter is concerned with interpreting human actions and meanings. The former is amenable to quantitative representation -- graphs -- while the latter is inherently linguistic and interpretive. The former has to do with estimation and causal analysis, while the latter has to do with interpretation and narrative.

Often, of course, historians are involved in both kinds of interpretation and analysis -- both measurement and interpretation.  So when Charles Tilly describes four centuries of French contention in The Contentious French, he is interested in charting the rising frequency of contentious actions (cliometric); but he is also interested in interpreting the intentions and meanings associated with those actions (hermeneutic).

New modes of historical presentation

Victor Lieberman's Strange Parallels: Volume 1, Integration on the Mainland: Southeast Asia in Global Context, c.800-1830 and Strange Parallels: Volume 2, Mainland Mirrors: Europe, Japan, China, South Asia, and the Islands: Southeast Asia in Global Context, c.800-1830 represent about 1000 pages of careful, dense historical prose extending over two volumes. As previously discussed (link, link), the book reviews a thousand years of history of the polities of France, Kiev, Burma, Japan, and China, it documents a significant correlation of timing across the extremes of Eurasia, and it offers some historical hypotheses about the causes of this synchronicity. It is a long and involved story.

My question here is perhaps a startling one: Is it possible that some alternative modes of presentation would permit the author to represent the heart of the historical findings much more efficiently in the form of a complex animated visual display? Could the empirical heart of the two volumes be summarized in the form of a rich data display over time? Is the verbal narrative simply a clumsy way of representing what really ought to be graphed? What would be gained, and what would be lost by replacing the long complex text with a compact series of graphs and maps?

This thought experiment is possible because Lieberman's argument lends itself to a quantitative interpretation. Essentially he is focusing on factors that can be estimated over time: degree of scope of a regime, degree of integration of institutions, economic productivity, agricultural intensity, rainfall, temperature, population level and density, mortality by disease, and transport capacity, for example. And he is looking for one or more factors whose temporal variations can be interpreted as a causal factor explaining correlations across the graphs. So we could imagine a master graph representing the factual core of the research, with six groups of graphs over time, representing the chief variables for each region over time.  Here is Lieberman's initial effort along these lines, graphing his estimate of "scope and consolidation" of the states of SE Asia and France.  And we can imagine presenting different data series representing his findings about agricultural productivity, mortality, population, climate change, etc., arranged around a single timeline.

We might imagine supplementing these superimposed data series with a series of dated maps representing the territorial scope of the states of the various regions, arranged along a timeline:

 France T1

 France T2

France T3

(A similar series would be constructed for the states of SE Asia.)

What this coordinated series of graphics represents, then, is the core set of facts that Lieberman has synthesized and presented in the book.  By absorbing the social, political, and economic changes represented by this graphical timeline, the reader has gained access for the full set of empirical claims offered by Lieberman.  And, one might say, the presentation is more direct and comprehensible than the verbal description of these changes contained in the text.  Moreover, we might expect that patterns will emerge more or less directly from these graphic presentations -- for example, the synchrony between state crisis and accumulating climate change.

As for what is lost in this version of the story -- several things seem clear. First, much of the narrative that Lieberman provides is synthetic. He attempts to pull together a wide variety of sources in order to arrive at a summary statement such as this: "The Capetian state increased dramatically in scope and administrative competence between 1000 and 1250." So the narrative serves to justify and document a particular inflection point in the long graph of "French polity". It provides the evidentiary basis for the estimate at this period in time.

Second, of course, the packet of graphs I've just described lacks the eloquence and vividness of the prose that Lieberman or other talented historians are able to achieve in telling their stories. It represents only the abstract summary of conclusions, not the nuance of the reasoning or the drama of the story. The prose text is inherently enjoyable to read, and it engages the reader to share the historical puzzle. But, one might argue, the epistemic core of the book is precisely the abstract factual findings, not the prose style. And the reasoning can be captured as a hypertext lying behind the graph -- a sort of annotated hyper-document.

Finally, this notion of arriving at an abstract, schematic representation of a history of something doesn't work at all for many kinds of historical writing. Michael Kammen's Mystic Chords of Memory: The Transformation of Tradition in American Culture is an inherently semiotic argument, working out the ways that public ceremonies and monuments work in the consciousness of a population. Robert Darnton's The Great Cat Massacre: And Other Episodes in French Cultural History is a deft interpretive inquiry, arriving at a complex interpretation of a puzzling set of actions. These examples of great historical writing are evidence-based; but they are not designed to allow estimation of a set of variables over time. And I don't see that there is the possibility of a more abstract and symbolic representation of the historical knowledge they represent.

One might say that what we have encountered here is an important fissure within contemporary historical writing, between "cliometric" research and knowledge (Reflections on the Cliometrics Revolution: Conversations with Economic Historians) and hermeneutic historical knowledge (Paul Ricoeur, Memory, History, Forgetting). The former is primary interested in the processes of change of measurable human or social variables over time, whereas the latter is concerned with interpreting human actions and meanings. The former is amenable to quantitative representation -- graphs -- while the latter is inherently linguistic and interpretive. The former has to do with estimation and causal analysis, while the latter has to do with interpretation and narrative.

Often, of course, historians are involved in both kinds of interpretation and analysis -- both measurement and interpretation.  So when Charles Tilly describes four centuries of French contention in The Contentious French, he is interested in charting the rising frequency of contentious actions (cliometric); but he is also interested in interpreting the intentions and meanings associated with those actions (hermeneutic).

New modes of historical presentation

Victor Lieberman's Strange Parallels: Volume 1, Integration on the Mainland: Southeast Asia in Global Context, c.800-1830 and Strange Parallels: Volume 2, Mainland Mirrors: Europe, Japan, China, South Asia, and the Islands: Southeast Asia in Global Context, c.800-1830 represent about 1000 pages of careful, dense historical prose extending over two volumes. As previously discussed (link, link), the book reviews a thousand years of history of the polities of France, Kiev, Burma, Japan, and China, it documents a significant correlation of timing across the extremes of Eurasia, and it offers some historical hypotheses about the causes of this synchronicity. It is a long and involved story.

My question here is perhaps a startling one: Is it possible that some alternative modes of presentation would permit the author to represent the heart of the historical findings much more efficiently in the form of a complex animated visual display? Could the empirical heart of the two volumes be summarized in the form of a rich data display over time? Is the verbal narrative simply a clumsy way of representing what really ought to be graphed? What would be gained, and what would be lost by replacing the long complex text with a compact series of graphs and maps?

This thought experiment is possible because Lieberman's argument lends itself to a quantitative interpretation. Essentially he is focusing on factors that can be estimated over time: degree of scope of a regime, degree of integration of institutions, economic productivity, agricultural intensity, rainfall, temperature, population level and density, mortality by disease, and transport capacity, for example. And he is looking for one or more factors whose temporal variations can be interpreted as a causal factor explaining correlations across the graphs. So we could imagine a master graph representing the factual core of the research, with six groups of graphs over time, representing the chief variables for each region over time.  Here is Lieberman's initial effort along these lines, graphing his estimate of "scope and consolidation" of the states of SE Asia and France.  And we can imagine presenting different data series representing his findings about agricultural productivity, mortality, population, climate change, etc., arranged around a single timeline.

We might imagine supplementing these superimposed data series with a series of dated maps representing the territorial scope of the states of the various regions, arranged along a timeline:

 France T1

 France T2

France T3

(A similar series would be constructed for the states of SE Asia.)

What this coordinated series of graphics represents, then, is the core set of facts that Lieberman has synthesized and presented in the book.  By absorbing the social, political, and economic changes represented by this graphical timeline, the reader has gained access for the full set of empirical claims offered by Lieberman.  And, one might say, the presentation is more direct and comprehensible than the verbal description of these changes contained in the text.  Moreover, we might expect that patterns will emerge more or less directly from these graphic presentations -- for example, the synchrony between state crisis and accumulating climate change.

As for what is lost in this version of the story -- several things seem clear. First, much of the narrative that Lieberman provides is synthetic. He attempts to pull together a wide variety of sources in order to arrive at a summary statement such as this: "The Capetian state increased dramatically in scope and administrative competence between 1000 and 1250." So the narrative serves to justify and document a particular inflection point in the long graph of "French polity". It provides the evidentiary basis for the estimate at this period in time.

Second, of course, the packet of graphs I've just described lacks the eloquence and vividness of the prose that Lieberman or other talented historians are able to achieve in telling their stories. It represents only the abstract summary of conclusions, not the nuance of the reasoning or the drama of the story. The prose text is inherently enjoyable to read, and it engages the reader to share the historical puzzle. But, one might argue, the epistemic core of the book is precisely the abstract factual findings, not the prose style. And the reasoning can be captured as a hypertext lying behind the graph -- a sort of annotated hyper-document.

Finally, this notion of arriving at an abstract, schematic representation of a history of something doesn't work at all for many kinds of historical writing. Michael Kammen's Mystic Chords of Memory: The Transformation of Tradition in American Culture is an inherently semiotic argument, working out the ways that public ceremonies and monuments work in the consciousness of a population. Robert Darnton's The Great Cat Massacre: And Other Episodes in French Cultural History is a deft interpretive inquiry, arriving at a complex interpretation of a puzzling set of actions. These examples of great historical writing are evidence-based; but they are not designed to allow estimation of a set of variables over time. And I don't see that there is the possibility of a more abstract and symbolic representation of the historical knowledge they represent.

One might say that what we have encountered here is an important fissure within contemporary historical writing, between "cliometric" research and knowledge (Reflections on the Cliometrics Revolution: Conversations with Economic Historians) and hermeneutic historical knowledge (Paul Ricoeur, Memory, History, Forgetting). The former is primary interested in the processes of change of measurable human or social variables over time, whereas the latter is concerned with interpreting human actions and meanings. The former is amenable to quantitative representation -- graphs -- while the latter is inherently linguistic and interpretive. The former has to do with estimation and causal analysis, while the latter has to do with interpretation and narrative.

Often, of course, historians are involved in both kinds of interpretation and analysis -- both measurement and interpretation.  So when Charles Tilly describes four centuries of French contention in The Contentious French, he is interested in charting the rising frequency of contentious actions (cliometric); but he is also interested in interpreting the intentions and meanings associated with those actions (hermeneutic).

Predictions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Predictions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Predictions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Causing public opinion

It is interesting to consider what sorts of things cause shifts in public opinion about specific issues. This week's national election is one important example. But what about more focused issues -- for example, the many ballot initiatives that were considered in many states? To what extent can we discover whether there is a measurable effect on public opinion by the organized efforts of advocacy groups through advertising and other strategies for reaching the minds of voters?

In these cases we might imagine that voters have a prior set of attitudes towards the issue -- perhaps including a large number of "don't know/don't care" people. Then a set of advocates form to lobby the public pro and con. They mount campaigns to influence voters' opinions towards the option they prefer. And on the day of the election voters will indicate their approval -- often in ratios quite different from those that were measured in pre-campaign surveys. So something happened to change the composition of public opinion on the issue. The question here is whether it is possible to estimate the effects of various possible influencers.

This seems like potentially a very simple area of causal reasoning about social processes. The outcome variable is fairly observable through polling and the final election, and the interventions are also usually observable as well, both in timing and magnitude. So the world may present us with a series of interventions and outcomes that support fairly strong causal conclusions -- for example, "each time ad campaign X hits the airwaves in a given market, there is an observed uptick in support for the proposition." It is unlikely that the correlation occurred as a result of random variations in both terms; we have a theory of how advertising influences voters; and we conclude that "ad campaign X was a causal factor in shaping voter opinion in this time period." (It is even possible that X played a role in both segments of opinion, resulting in an up-tick in both yes and no responses. Then we might also judge that X was effective at polarizing voters -- not the effect the strategist would have aimed at.)

This is an example of singular causal reasoning, in that it has to do with one population, one issue, and a specific series of interventions. What would be needed in order to arrive at a conclusion with generic scope -- for example, "advertising along the lines of X is generally effective in increasing support for its issue"? The most straightforward argument to the generic conclusion would be a study of an extended set of cases with a variety of strategies in play. If we discover something like this -- "In 80% of cases where X is included in the mix it is observed to have a positive effect on opinion" -- then we would have inductive reason for accepting the generic causal claim as well. This is basic experimental reasoning.

Take a hypothetical issue -- a referendum on a proposal for changing the system the state uses for assessing business taxes. Suppose that a polling firm has done weekly polling on the question and has recorded "yes/no/no opinion" since October 2007. Suppose that two organizations emerged in December to advocate for and against the proposal; that each raised about $5 million; and that each included an advertising campaign in its strategy. Suppose further that the "no" campaign also included a well-organized effort at the parish level to persuade church members to vote against the measure on religious grounds and the "yes" campaign included a grassroots effort to get university students and staff to be supportive of the measure on pro-science and pro-economy grounds. And suppose each organization mounted a "new media" campaign using email lists and web comminication to make its case. Finally, suppose we have good timeline data about the occurrence and volume of media spots throughout the period of June through November.

This scenario involves three types of causes, a timeline representing the application of the interventions, and a timeline representing the effects. From this body of data can we arrive at estimates of the relative efficacy of the three treatments? And does this set if conclusions provide credible guidance for other campaigns over other issues in other places?

There is also the question of the efficacy of the implementation of the strategies. Take the ad campaigns. Whether a specific campaign succeeds in changing viewers' opinions depends on the content, message, and production quality. Does the message resonate with a target segment of voters? Does the production design stimulate emotions that will lead to the desired vote? So evaluating efficacy needs to be done across instances of media as well as across varieties of media. (This is the function of focus groups and snap polls -- to evaluate the effects of specific messages and production choices on real voters.)

(Here is a link to some information about the process leading up to a positive vote on the Michigan Stem Cell initiative this month. A good general introduction to the social psychological theories about the formation of attitudes and opinions is Stuart Oskamp and P. Wesley Schultz, Attitudes and Opinions.)

Causing public opinion

It is interesting to consider what sorts of things cause shifts in public opinion about specific issues. This week's national election is one important example. But what about more focused issues -- for example, the many ballot initiatives that were considered in many states? To what extent can we discover whether there is a measurable effect on public opinion by the organized efforts of advocacy groups through advertising and other strategies for reaching the minds of voters?

In these cases we might imagine that voters have a prior set of attitudes towards the issue -- perhaps including a large number of "don't know/don't care" people. Then a set of advocates form to lobby the public pro and con. They mount campaigns to influence voters' opinions towards the option they prefer. And on the day of the election voters will indicate their approval -- often in ratios quite different from those that were measured in pre-campaign surveys. So something happened to change the composition of public opinion on the issue. The question here is whether it is possible to estimate the effects of various possible influencers.

This seems like potentially a very simple area of causal reasoning about social processes. The outcome variable is fairly observable through polling and the final election, and the interventions are also usually observable as well, both in timing and magnitude. So the world may present us with a series of interventions and outcomes that support fairly strong causal conclusions -- for example, "each time ad campaign X hits the airwaves in a given market, there is an observed uptick in support for the proposition." It is unlikely that the correlation occurred as a result of random variations in both terms; we have a theory of how advertising influences voters; and we conclude that "ad campaign X was a causal factor in shaping voter opinion in this time period." (It is even possible that X played a role in both segments of opinion, resulting in an up-tick in both yes and no responses. Then we might also judge that X was effective at polarizing voters -- not the effect the strategist would have aimed at.)

This is an example of singular causal reasoning, in that it has to do with one population, one issue, and a specific series of interventions. What would be needed in order to arrive at a conclusion with generic scope -- for example, "advertising along the lines of X is generally effective in increasing support for its issue"? The most straightforward argument to the generic conclusion would be a study of an extended set of cases with a variety of strategies in play. If we discover something like this -- "In 80% of cases where X is included in the mix it is observed to have a positive effect on opinion" -- then we would have inductive reason for accepting the generic causal claim as well. This is basic experimental reasoning.

Take a hypothetical issue -- a referendum on a proposal for changing the system the state uses for assessing business taxes. Suppose that a polling firm has done weekly polling on the question and has recorded "yes/no/no opinion" since October 2007. Suppose that two organizations emerged in December to advocate for and against the proposal; that each raised about $5 million; and that each included an advertising campaign in its strategy. Suppose further that the "no" campaign also included a well-organized effort at the parish level to persuade church members to vote against the measure on religious grounds and the "yes" campaign included a grassroots effort to get university students and staff to be supportive of the measure on pro-science and pro-economy grounds. And suppose each organization mounted a "new media" campaign using email lists and web comminication to make its case. Finally, suppose we have good timeline data about the occurrence and volume of media spots throughout the period of June through November.

This scenario involves three types of causes, a timeline representing the application of the interventions, and a timeline representing the effects. From this body of data can we arrive at estimates of the relative efficacy of the three treatments? And does this set if conclusions provide credible guidance for other campaigns over other issues in other places?

There is also the question of the efficacy of the implementation of the strategies. Take the ad campaigns. Whether a specific campaign succeeds in changing viewers' opinions depends on the content, message, and production quality. Does the message resonate with a target segment of voters? Does the production design stimulate emotions that will lead to the desired vote? So evaluating efficacy needs to be done across instances of media as well as across varieties of media. (This is the function of focus groups and snap polls -- to evaluate the effects of specific messages and production choices on real voters.)

(Here is a link to some information about the process leading up to a positive vote on the Michigan Stem Cell initiative this month. A good general introduction to the social psychological theories about the formation of attitudes and opinions is Stuart Oskamp and P. Wesley Schultz, Attitudes and Opinions.)