Introduction to an Introduction

This book has been edited with the explicit idea of allowing the reader to imagine that virtual histories can be generated in a computer in the same way as in her/his mind. This is not a literary exercise, however, but an example of a radical revolution in the way of doing History as a social science.

The essays present in this book are the result of a special session organized during the annual conference of the European Social Simulation Association (ESSA) held at the Autonomous University of Barcelona (Spain) on September 2014. “Simulating the Past to Understand Human History”—SPUHH—for the first time in an ESSA con-

J.A. Barcelo (s)

Department of Prehistory, Universität Autònoma de Barcelona, Bellaterra, Spain e-mail: juanantonio.barcelo@uab.cat

F. Del Castillo

National Scientific and Technical Research Council (CONICET), Puerto Madryn, Argentina e-mail: delcastillo@cenpat-conicet.gob.ar

© Springer International Publishing Switzerland 2016 1

J.A.

Most contributions are studies of the most remote past: prehistory and archaeol­ogy. But it does not mean that other historical periods cannot be made understandable recreating what people did and believed within a computer. In practice, then, the virtual pasts we can recreate within a computer are accessible in the sense that they tend to realign this paradigmatic new way of understanding the past with both the commonsense trivial idea that history is about what people did in the past (Düring 2014; Lake 2015; Lercari 2016; Cegielski and Rogers 2016; Marwick 2016).

1.1.1 A “New” Way of Understanding Human History?

History is a science that should look for causal affirmations about the formation processes of society. Therefore, the startpoint of historical research should be explaining past social events by showing how human behavior fit into a causal structure, that is to say, a vast network of interacting actions and entities, where a change in a property of an entity dialectically produces a change in a property of another entity (transformation).

This focus on the causal understanding of historical processes fits well with the notion that archaeology and history should offer something to contemporary society as an integrated science of long-term societal change and human-environment interaction (Rashevsky 1968; Abbott 1983; Turchin 2008, 2011; Hurley 2012; Gavin 2014; Lake 2015; Cegielski and Rogers 2016).

Descriptive chains of events, even if true, are not explanations but they are something to be explained. Clearly, nothing is gained if we introduce as an explanation of why some x occured, an indicator that some y occurred before or after (where x and y refer to different acts, events or processes). In some sense, causal interactions are the factors explaining why a social action was performed at a specific time and place, which is, its motivation or reason.

We can understand social action in the past only in terms of how humans did it. It is easy to see then that the concept of mechanism becomes the heart of this kind of causal explanation. Obviously, the word “mechanism” is here a parable of how social intentions, goals and behaviors are causally connected. A “social mecha­nism” should then explain how social activity worked, rather than why the traits contributing to these activities or workings are there (Bechtel and Richardson 1993; Machamer 2002; Craver 2001; Darden 2002; Glennan 2002; Gerring 2008; Yli- koski 2011; Maurer 2016). “Mechanisms are entities and activities organized such that they are productive of regular changes from start or set-up to finish or termi­nation conditions” (Machamer et al. 2000, p. 3). No matter how long or compli­cated the causal process is, it can be called a mechanism if its description answers the question how did the cause bring about the effect.

We are adopting an analytical approach in which “social facts” are seen as generated, triggered, produced, brought about or “caused” by actions which themselves are in some sense “caused,” or at least partly determined by the con­straints presented by the social environments and situations in which such actions take place (Elster 1989).

These prospective for a new way of understanding human history are strongly related with current developments in Analytical Sociology. Such a term officially entered the sociological vocabulary with Hedstrom’s Dissecting the Social (Hed- strom 2005) to denote the sociological perspective that seeks systematically to formulate and empirically test micro-founded, mechanism-based explanations of complex macro-level patterns and dynamics (see also: Bortolini 2007; Hedstrom and Bearman 2009a, b; Racko 2011; Raub et al. 2011; Bearman 2012; Edling 2012; Wan 2012; Opp 2013; Manzo 2010; 2014; Lombardo 2015). According to such definition, we can envisage a kind of “Analytical history” when trying to under­stand complex chains of change in terms of the discovery of patterns in transitions. To build such a discipline, and paraphrasing Manzo (2014), we should modify the actual way of describing the past and:

1. using concepts that are as clear and precise as possible to describe both the facts to be explained and the explanatory hypotheses/facts mobilized to explain them, while avoiding all linguistic obscurity and convolutedness (Pomeranz 2011),

2. mobilizing the best quantitative and qualitative empirical information available and use the technical tools best suited to describing the facts to be explained,

3. making emphasis on the social outcome(s) evidenced somewhere and some- when to understand what happened and why. This can be done by first for­mulating a “generative model” that is, a model of a set of mechanisms, where a mechanism is a set of entities and activities likely to trigger a sequence of events (i.e., a process) likely to bring about the outcome(s),

4. providing a realistic description of the relevant micro-level entities and activities assumed to be at work, as well as the structural interdependencies in which these entities are embedded and their activities unfold,

5.

6. comparing the predictions made by the generative model with the empirical description of the historical facts to be explained in order to assess the gener­ative sufficiency of the mechanisms postulated,

7. injecting as much empirical data as possible into the generative model in order to prove that the hypothesized assumptions are not only generative sufficient but also empirically grounded, and reanalyze its behavior and high-level consequences.

A common objection to employing mathematical and formal models in the study of historical dynamics is that social systems are so complex that any mathematical model would be a hopeless oversimplification without any chance of telling us interesting things about these systems. As Turchin (2008, 2011) has argued, this argument is wrong: when any model appears to be “complex” then, the only way to analyze its behavior is through objective measuring and using mathematical lan­guage. “Naked” human brain is not a bad tool for extrapolating linear trends, but it fails abysmally when confronted with systems of multiple parts interconnected with nonlinear feedback loops. We need mathematical formalism to express our ideas unambiguously, and both analytical methods and fast computers to determine the implications of the assumptions we made (West 2011).

The advantage of formal modeling is that, by making explicit and unambiguous the relationships between events and also the intended scope, it is easier to deter­mine whether the model is supposed to be applicable to some observed phe­nomenon and, if so, whether it adequately fits it (Lake 2015; Nakoinz and Knitter 2016).

1.1.2