Conclusions. Rethinking the Way the Past Can Be Made Understandable

As suggested by Saqalli and Baum in their contribution to this volume (Chap. 8), three goals may be assigned to modelling past social dynamics: describing, understanding and predicting.

By conceptualizing a dynamical and extensible simulation platform, researchers and domain experts are provided with a tool for specifying and formulating own hypotheses, assumptions and discoveries (Timm et al., Chap. 2). Crucially, the simulation itself is claimed to carry the central explanatory role: it is the fit of the generated data, or the identification of generating agents and their rules of behavior, that purportedly does the explaining. The simulation either may provide a test of the models and its underlying theory, if any, or may simply allow the experimenter to observe and record the behavior of the target system. As the emphasis shifts from describing the behavior of a target system to the proper understanding of social systems through time, so the objective of historical research changes to the experimental manipulation of a possible scenario.

A common place of development of the historical simulations should be this focus on the understanding of the processes underlying social change, the evolution of the simulation with the representation of stability and change of social simulated process. May be this is what really matters to us, understanding the process and not only making emphasis on the results and the possible predictions of the models. The way an economist or a sociologist mainly seek to understand analytical results to understand the models is a clear example. To simulate is to understand how a model behaves, a social historical model is a formalization of an historical explanation and it is also the explicitation of all the assumptions implicit in the proposed model. The main value of creating artificial societies is not to create new entities for their own sake, but observing theoretical models performing on a testbed.

Computer simulation imitates the past through a computer reproduction of individual actions of agents, in response to a historically justified calibration of the virtual world in which they moved (Caughey 1972). Thus computed results are obtained as the effects of individual actions in a virtual environment and the impact that these environment has over the agents. The fact that the results “fit” with the empirical description of change it does not mean that the causes of changes are isomorphic to the actions implemented in the simulation. A computational model that is able to generate results similar to the available empirical evidence of a historical situation is necessary but not sufficient for explanation (Guner-Yanoff 2009).

It is important to recognize that, as a series of languages, rather than as a single technique, computer simulation can be used for different purposes and in a variety of theoretical frameworks. The use of artificial intelligence theories and techniques offers different advantages to scholars with a “post-modern” or hermeneutic idea of humanities. On the one hand it is important to mention the current trend on cog­nitive modeling and belief-desire-intention architectures for designing more “human-like” computer agents. It may help researchers in exploring non rational ways of decision making. On the other hand, the fact that a virtual past resides in a computer and it can be modified according to the needs of the human agent interacting with it contributes to change the traditional idea of the immutability of scientific theories. A computer program can be modified and altered at any time, and the consequences of modifications are immediately available to the user. Explanations appear to be as a result as flexible tools in the hands of people, used for anything the user need.

Is this a radically new way of understanding the past? In 2001, R. Hanson wrote: “We expect our descendants to run historical simulations for several different kinds of reasons. First, some historical simulations will be run for academic or intellectual interest, in order to learn more about what actually happened in the past, or about how history would have changed if conditions had changed. Other historical sim­ulations, however, perhaps the vast majority, will be created for their story-telling and entertainment value” (Hanson 2001).

Turchin (2008, 2011) considers there are two major reasons explaining this failure. First, computational simulation has been inspired directly by successes in physical sciences. Yet physicists traditionally chose to deal with systems and phenomena that are very different from those in history. Physicists tend to choose very simple systems with few interacting components (such as the solar system, the hydrogen atom, etc.) or with systems consisting of a huge number of identical components (as in the modynamics). As a result, very precise quantitative predic­tions can be made and empirically tested. But even in physical applications, such systems are rare, and in social sciences only very trivial questions can be reduced to such simplicity.

If these two problems are the real reason why previous attempts failed, then some recent developments in natural sciences provide a basis for hope. First, during the last 20-30 years physicists and biologists have mounted a concerted attack on complex systems. A number of approaches can be cited here: nonlinear dynamics, synergetics, complexity, and so on. The use of powerful computers has been a key element in making these approaches work. Second, biologists, and ecologists in particular, have learned how to deal with short and noisy datasets. Again, plentiful computing power was a key enabler, allowing such computer-intensive approaches as nonlinear model fitting, bootstrapping, and cross-validation.

The main challenges for historical disciplines in operationalizing computational concepts in a science of long-term social dynamics is how can we systematically track and explain non-linear chains of causality that cascade from multi-scale inter-actions among individuals, groups, and the biophysical world up to the emergent level of social organization. This is especially difficult when traditional analyses and narratives are inherently linear and our knowledge of the past is static. Some equation-based models of human behavioral ecology and related approaches can account for non-linear dynamics (Levin et al. 2013; Anderies 2015). However, even these models have difficulty in adequately dealing with the kinds of multi-scale interactions of many spatially and culturally heterogeneous, indepen­dent actors. Furthermore, even with a firm understanding of the dynamics of human societies, how can we recognize and account for historical complex dynamics when key features are not preserved in the historical record? Meeting these challenges will require the development and application of robust theory about drivers and nature of long-term social change. Of course, it is impossible to carry out real-world experiments with past human systems—or even with modern ones at the scales of interest to most historians and archaeologists. Computational simulation modeling offers a valuable protocol for combining social theory and historical knowledge to create experimental environments in which to explore non-linear causality in complex systems and generate results that can be evaluated against the empirical historical data.

Therefore, we want to close this introduction to simulating the past theories, techniques and technologies remembering that the starting point of the explanation of prehistoric and ancient times by means of computer methods is not the creation of a particular artificial society that may reproduce what really happened in a remote past but the investigation of the mathematically possible development of specific classes of model systems (pure systems). As these pure systems usually generate a lot more different paths of development than are known from real human history, we should limit these possibilities by introducing constraints from well documented historical and ethnographical narratives or from archaeological data. The histori­cally interesting question is then why these constraints appeared in reality. This approach can be traced back to Gibson's formulation of affordance theory (Gibson 1977, 1979). The relationship between successive historical events afforded by a potential causal factor can be termed an affordance. On this view, an event's

historical function reflects the actions that may have been performed there and then by social agents, given both the particular situation (context) and the apparent directionality of the trajectory configured by previous events. In other words, the future state of a society is not predefined as a form of destiny, but there is a sense of directionality in the historical sequence of social actions with a dynamic, sometimes even adaptive, nature. Therefore, understanding those elements of the past that have been seen in the present assumes that the perceived strength of causes can be analyzed under the form of particular connections between the potential cause and the observed effect (Van Overwalle and Van Rooy 1998). In this way, we can understand that affordances are not properties, or at least not always properties (Chemero 2003). Affordances are relations between the abilities of people, their intentions, and what the concrete situation allow to be performed.

For this sort of “affordance-based” explanation be operative, the historian should discover what precipitating conditions generate an increase in the probability of the historical occurrence of an action at some place and moment and constrained by the social and environmental context. Beyond a simple addition of individual random decisions, what happened in the past should be defined in terms of social dispo­sitions or capacities within a system of subjects, intentions, activities, actions and operations, some of them rational, others clearly indeterminate, impulsive or unconscious. The fact that the performance of some social action A, in circum­stances T, had a probability P of having caused a change Y in some entity N (social agent, community of social agents or the nature itself), is a property of the social action A. It is a measurement of the intensity of the propensity, tendency, or inclination of certain events to appear in determined causal circumstances. In general, if the potentiality (occurring in a state S) to have state property X has led to a state S' where indeed X holds, then this state property X of state S' is called the fulfillment or actualization of the potentiality for X occurring in state S. A social action or sequence of social actions will be causally related with a state change if and only if the probability for the new state is higher in presence of that action that in its absence. Causal significance of a factor C for a factor E corresponds to the difference that the presence of C makes on E. That is, observed changes in the historical record of that particular event are not necessary determined univocally by the agent's will alone, but there is some probability that in some productive, dis­tributive or use contexts, some values are more probable than others are. We are not suggesting that the cause is a probabilistic relationship, but it should be expressed probabilistically given the implicit uncertainty and the lack of any direct reference to what really happened there and then. In these circumstances, a historical situation should be defined as a relatively constant background condition consisting of possible stimuli afforded by the situation itself (the social agents and their envi­ronment). Thus, the primary explanandum of historical theory is social capacities: the capacity to work, to produce, to exchange, to interact, to obey, to impose something or someone. Social action appears as transformational processes to which social scientists attribute the achievement of some new state of the world: an end, goal, or result.

In any case, the purpose should be not to replicate the actual processes of historical change, but to obtain useful insights in terms of potentialities, dispositions or causal powers to construct a model to explain the long term social dynamics. Therefore, the starting point of the explanation of social systems by means of computer simulation is not the simulation of one particular system but the inves­tigation of the mathematically possible development of specific classes of model systems (pure systems). As these pure systems usually generate a lot more different paths of development than are known from real human history, the automated archaeologist has to limit these possibilities by introducing known social constraints from social reality. The socially interesting question is then why these constraints appeared in reality. This particular procedure is aptly described by Bateson with the concept of “cybernetic explanation” (Bateson 1957).

We hope that a future society will very likely have the technological ability and the motivation to create large numbers of completely realistic social simulations. Simulated worlds created by such a future society to solve policy, strategic and research issues would most likely be retrospectives, i.e., historical simulations in which artificial intelligence would genuinely address human matters, rather than merely playing the role of a surrogate. These simulations will provide a rich source of information to a future society about how it arrived at its current stage of development as well as how it could avoid repeating the mistakes of the past. Someone has proposed that this future is very near, ca. 2050 (Jenkins 2006). We will rewrite this introduction in 35 years!

Acknowledgments As a direct consequence of an international conference, this chapter (and even the book) could not have been possible without the contribution of many people. We thank all the students and colleagues that contributed to the organization. Special thanks are due to Enrico Crema, Marco Madella, Laura Mameli, Francesc Xavier Miguel Quesada, Flaminio Squazzoni, Xavier Rubio-Campillo and Xavier Vilà. The conference and the book arrived to a safe harbor thanks to founding by the Universitat Autònoma de Barcelona, The Catalan Government-Generalitat de Catalunya (2014 SGR 1169), The Spanish Ministry of Science and Innovation, through Grant No.HAR2012-31036 awarded to J.A. Barcelo and Project CSD2010-00034 «Social and environmental transitions: Simulating the past to understand human behavior (CONSOLIDER-INGENIO 2010 program by Spanish Ministry of Science and Inno­vation, see: http://www.simulpast.es).

We also acknowledge all people at Springer Verlag for their efforts and enthusiasm with this book, and obviously to all authors and contributors to this volume.