Simulating the Recent Past

There are two key factors that can be used to fix the begining of “modernity”: industrial revolution at the end of 18th century and the French revolution and the posterior historical trend towards parliamentary political regimes.

The Industrial Revolution was the transition to new manufacturing processes in the period from about 1760 to sometime between 1820 and 1840. This transition included going from hand production methods to machines, new chemical manufacturing and iron production processes, improved efficiency of water power, the increasing use of steam power, and the development of machine tools. It marks a major turning point in history; almost every aspect of daily life was influenced in some way. In particular, average income and population began to exhibit unprecedented sustained growth, but also new forms of inequality emerged. Only some aspects of this series of historical events have been explored using computational methods (but see Atack 1979; Komlos 1989; Komlos and Artzrouni 1994; Foley 1998; Malerba et al. 1999 and Garavaglia 2010; Spaiser and Sumpter 2016). Harley and Crafts (2000) used a classical computational general equilibrium (CGE) trade model with diminishing returns in agriculture and realistic assumptions about consumer demand. Their results show that while technical change in cottons and iron were major spurs to exportation of those specific goods, the need for food imports also stimulated exports generally. In any case, why did England industrialize first? And why was Europe ahead of the rest of the world? To answer these questions, Voigtlander and Voth (2006) built a probabilistic two-sector model where the initial escape from Malthusian constraints depends on the demographic regime, capital deepening and the use of more differentiated capital equipment. Weather-induced shocks to agricultural productivity cause changes in prices and quantities, and affect wages.

In a standard model with capital externalities, these fluctuations interact with the demographic regime and affect the speed of growth. Voigtlander and Voth model has been calibrated to match the main characteristics of the English economy in 1700 and the observed transition until 1850. The authors capture one of the key features of the British Industrial Revolution emphasized by economic historians— slow growth of output and productivity. Fertility limitation is responsible for higher per capita incomes, and these in turn increase industrialization probabilities. Simulations using parameter values for other countries show that Britain's early escape was only partly due to chance. France could have moved out of agriculture and into manufacturing faster than Britain, but the probability was less than 25 %. Contrary to recent claims in the literature, 18th century China had only a minimal chance to escape from Malthusian constraints (Zhou 2008). This line of enquire has also been further explored by Galor et al. (2009), Desmet and Parente (2012), Mejia Cubillos (2015). Social aspects of emerging inequality as a consequence of industrialization have been explored by Crayen and Baten (2010). The third industrial revolution that is the transition to the Information Age has been computationally explored by Veneris (1990).

The French Revolution of 1789 was much more than a mere uprising of the “people” against the State. Its historical relevance comes from the fact that the main guidelines for the future parliamentary forms of government were defined then (Sharp and Weisdorf 2012). Why some street fighting in Paris at that time could have had so enduring consequences? Although there is not any specific computer simulation of what happened in France at the end of eighteenth century, we may suggest a general model of a social revolution based on a conjunction of events that were, themselves, and each independently caused (Grossman 1991). These events would include state crises, popular uprisings and elite actions.

As they unfolded, these events may have been shaped by international forces that would have impinged on the states in question. In response to these events, the state and other elite actors may have found themselves constrained by some crisis (typically financial, often deepened by the exigencies of war) and therefore increasingly susceptible to the revolutionary challenges. The role for quasi-independent social actors, and the historically unique forms and sequencing of events can make the model suppler, and better able to represent diverse scenarios than prior theories. As an example, we can consider the MASON RebeLand model (Cioffi-Revilla and Rouleau (2010), based on: (i) an explicit polity model with politically complete structure and processes; (ii) social and natural model components within integrated socio-natural systems; and (iii) generative dynamics where insurgency and the state of the polity (stable, unstable, failing, failed, and recovering) occur as emergent phenomena under a range of social and environmental conditions.

In other words, instead of “reproducing” the “storming of the Bastille” or the activities of Robespierre and his committee of Public Safety we can “calibrate” an abstract model of the causal factors of insurrection, civil conflict and political transformation with empirical data from historical sources. This approach was suggested by Sewell (1985), Skocpol (1985), Goldstone (1991), and computationally enriched by Squazzoni (2008a, b), Cederman et al. (2010), Sallach (2010), and Altaweel et al. (2012). In this way, we can investigate the outbreak of different historical situations (Hermann and Hermann 1967; Bremer 1977; Mintz 1981; Hanneman 1988; Schrodt 1988; Chadwick 2000; Fogu 2009). Beyond the emergence of social conflict, revolutions and uprisings, the historical process towards parliamentary political regimes can be formally explored using computational tools (Cederman 2001, 2005; Ulfelder and Lustik 2007). The historical origins and emergence of political democracy should be studied as a macro-historical process that expanded from a small number of democracies to about 50 % of all states.

In order to account for this development, Cederman and Gleditsch (2004) introduced an agent-based model combining natural-selection logic with adaptive mechanisms of regime change. The latter is implemented as an empirically calibrated, contextual rule that prompts democratization as an 5-shaped function of the democratic share of a state's immediate neighborhood. A similar transition rule governs regime change in the opposite direction. The computational results show that regime change and collective security are necessary to produce realistic trajectories of democratization at the systemic level.

Kroneberg and Wimmer (2012) have explored some historical aspects of France socio-political evolution from 1500 to 1900. They have analyzed in formal and computational terms the conditions under which political modernization lead to nation building, to the politicization of ethnic cleavages, or to populism by modeling these three outcomes as more or less encompassing exchange relationships between state elites, counterelites, and the population. The authors show how social actors seek coalitions that grant them the most advantageous exchange of taxation against public goods and of military support against political participation (see also Wimmer 2014).

Sandberg (2011) and Jansson et al. (2013) have experimented system dynamics for studies of the global diffusion of democracy from 1800 to 2000. The dynamic explanation proposed focuses on transitions to democracy, soft power, and communication rates on a global level. The analysis suggests that the transition from democratic experiences (‘the soft power of democracy') can be estimated from the systems dynamics simulation of an extended adoption-of-innovations model. Soft power, fueled by the growth in communications worldwide, is today the major force behind the diffusion of democracy. The findings indicate the applicability of system dynamics simulation tools for the analysis of political change over time in the world system of polities.

These are not the only historical subjects that can be explored using computational simulation tools and techniques.

The amount of information coming from sources as historical census has allowed an interest for simulating demographic trends from the recent past (Silverman et al. 2011, 2014). An early example of this trend is Whitmore's work on simulating Amerindian depopulation in colonial Mexico (Zubrow 1990; Whitmore 1992). Gonzalez-Bailon and Murphy (2013) have built an agent-based simulation, incorporating geographic and demographic data from nineteenth-century France, to study the role of social interactions in fertility decisions. The simulation made experimentation possible in a context where other empirical strategies were precluded by a lack of data. The authors evaluated how different decision rules, with and without interdependent decision-making, caused variations in population growth and fertility levels. The analyses show that incorporating social influence into the model allows empirically observed behavior to be mimicked, especially at a national level. These findings shed light on individual-level mechanisms through which the French demographic transition may have developed. Bar and Leukhina (2010) have worked on the demographic transition related with the industrial revolution (see also Skirbekk et al. 2015). Going beyond pure demographic models, Wu et al. (2011) have created an agent-based simulation of the spatial evolution of the historical population in China. (See also Zhao 2000).

Also related with the modeling of population trends in the recent past, there is an increasing interest in historical changes in land use as a subject of computer simulations to understand the evolution of modern cities and urbanization processes (Ruggles 1993; Zhao 1994; Parker et al. 2003; Manson 2005; Matthews et al. 2007; Entwisle et al. 2008; Rindfuss et al. 2008; Arce-Nazario 2007; Bretagnolle and Pumain 2010; Komlos and Kim 1990; Le et al. 2010; Fu et al. 2010; Long et al. 2014; Magliocca et al. 2015; Chang-Martinez et al. 2015; Heppenstall et al. 2016). In many cases, cellular automata and agent integrated models are developed based on the prior research of to simulate land use and cover change.

In such integrated cases, every cell is used to store the land use change related information of the location where the cell posits, and sense the land use change information of the cells in the neighborhood. Agents, with different roles, calculate the information stored in the cells and do the logistic decision of whether the cells change their states. Therefore, the model has capability of complex computation and a global dynamics.

Gasmi et al. (2015) propose a methodology to build agent-based models of the management of floods in Ha Noi (Viet Nam) in 1926. The authors have collected, digitized and indexed numerous historical documents from various sources, built a historical geographic information system to represent the environment and flooding events and finally designed an agent-based model of human activities in this reconstructed environment. They then show how this model can be useful to understand the decisions made by the different actors during this event, testing multiple scenarios and answering several questions concerning the management of the flooding events.

A possible criticism about the idea of simulating the past and the analytical explanation of social dynamics that generated our social, economic, political and cultural present would be the impossibility of simulating the historical evolution of complex polities in modern times for reasons of scale: to be fully capable of understanding historical dynamics of ancient empires and modern nations we would need to create artificial societies of such complexity that any computer could run the simulation. Nevertheless, the current use of agent-base modeling and related techniques to understand modern economics and modern social and political organization clearly indicates the opposite. If we consider the number of actual publications, it would seem that simulating the present is easier than simulating the past, and that simulating the recent past should be easier than simulate the most ancient human societies (Tesfatsion 2002; Batty 2007; Squazzonni 2012; Cioffi-Revilla 2014).

The amount of qualitative and quantitative historical data about the recent present may allow the historian-computationalist to go beyond the generic and abstract scales we have detailed up to now (households, families, communities, institutions, etc.) and introduce the replica of real people that once existed and we know for certain what they really did. Saqalli and Baum (Chap. 8) suggest the idoneity of two general scales for the computer simulation of social dynamics:

• The level of the village/hamlet (defined here along the more adequate word “terroir”) unit is often used because it is the functional unit of management of a landscape, the geographic expression of a combination of rationalities that have to interact altogether. Building a model of one simulated entity below this level is impossible regarding the importance of such interactions, both direct (marriages and other social interactions but also mutual manpower support for instance). Roughly, it is the level in which micro-economic rationality can be considered in order to analyze and explain differences in the use of natural resources;

• The level of the territory that corresponds to a culture or a group of cultures. Roughly, it is the level in which macro-economic rationality can be assessed, assuming a certain homogeneity regarding the use of natural resources within this culture comparing to others. A main aspect here is to analyze the impacts of a homogenous use of these resources;

Would it be possible going beyond those general scales? Some experiments have been published to consider the historical simulation at the level of the individual. Yang et al. (2010) propose the use of a pattern oriented inverse simulation (PIS) to analyze a particular family line with more successful candidates in the civil service examination in imperial China. Two relevant patterns observed in the real family system are employed to decode family strategies along such an elite family line. The authors implemented PIS through inverse simulation techniques, by fitting the simulated results to the real genealogical data arranged in time-series as patterns. In case all those techniques allow us to use the individual as a real unit of analysis in a history study, then can we simulate the past at the level of what historic people really did? For instance, can we create a model for European artistic development in the last centuries with virtual simulations of known artists and musicians? We have detailed life stories of those individuals and given the current technology of agent based systems, there is no doubt that we can recreate the world, understanding individual behavior (Düring et al. 2011; Novak et al. 2014). Some pioneering work on this line has been initiated by Schich et al. (2014), Medina (2014), Park et al. (2015). The same could be made for other historical events, like World War II, from the point of view of Churchill, Himmler, Guderian, Montgomery, Eisenhower or any private that we know fought on that war and whose actions affected other people (see Alexander and Danowski 1990; Wetherell 1998; Gould 2003; Lemercier 2005, 2012; Boyer 2008; Hamill and Gilbert 2009).

Simulating historical events in full detail can be enormously costly, however. Therefore most computer simulations today vary the detail at which they simulate various events. In general, the level of detail appropriate for any one place depends on how much more expensive it is to produce such detail, and on how influential larger errors are in producing errors in the final results of interest. Since it is harder to vary the simulation detail in role-playing simulations containing real people, these simulations tend to have some boundaries in space and time at which the simulation ends (Hanson 2001).

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Source: Barcelo Juan A., Del Castillo Florencia (eds.). Simulating Prehistoric and Ancient Worlds. Springer,2016. — 410 p.. 2016

Simulating the Recent Past

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