Predicting the Future

As it has been shown all along this introductory chapter, the use of simulations that integrate disparate quantitative time series data and other time-oriented information into a unified formal presentation can reveal patterns, causes, probabilities, and possibilities across complex social, technological, economic, and political systems.

One of the most influential arguments against scientific history was formulated by the philosopher Karl Popper (1957). Popper's main point was that because the future course of human history is critically affected by the development of knowledge, and because future scientific and technological discoveries cannot be predicted, a predictive science of human history is in principle impossible. How­ever, the notion of prediction in science is not limited to forecasting the future.

In fact, the future is in principle unpredictable. In social life rare events with huge consequences, occur with greater frequency than in purely physical applica­tions (Taleb 2010). The difference, however, is quantitative, not qualitative. Bridges collapse, space shuttles explode, and hurricanes strike from seemingly blue skies. However, we do not decide, on the basis of such prediction failures, that there are no laws of physics. Prediction is an inherent part of science, but not in the narrow sense of forecasting the future. Scientific prediction (to distinguish it from the common usage, which is closer in meaning to “prophecy”) is used in empirical tests of scientific theories. Scientific prediction inverses the logic of forecasting: whereas in making forecasts we assume the validity of the underlying theory and want to know what will happen to observables, in a scientific prediction exercise we want to use the degree of match between observables and predictions to infer the validity of the theory (Turchin 2008, 2011).

Thinking about the future can take numerous forms, varying from planning actions to foreseeing possible scenarios by means of knowledge and informed guesses, or speculations and intuitions, or imagination and creativity (Von Stack- elberg 2009; de Vito and Della Sala 2011). We are limiting the possibilities here to what can be formally extrapolated from the knowledge of the past. If there some kind of linearity in the dependence relationships empirically determined between temporally ordered events, we can extrapolate new events using linear and non-linear multiple regression statistical methods (Kantz and Schreiber 2004).

To our surprise, many aspects of human life can be extrapolated to some comparatively near future using relatively simple statistical models. This approach has had certain success in economy, as a side effect of path analysis studies (Nelson and Winter 1982; David 2001; Garrouste and Ioannides 2001; Hojer and Mattsson 2000; Martin and Sunley 2006; Vergne and Durand 2010). Chen et al. 2003 have presented a novel methodology for predicting future outcomes that uses small numbers of individuals participating in an imperfect information market. By determining their risk attitudes and performing a nonlinear aggregation of their predictions, we are able to assess the probability of the future outcome of an uncertain event and compare it to both the objective probability of its occurrence and the performance of the market as a whole.

In the same way, the future of political issues can also be examined from the point of view of the directed temporal dependencies among a set of social or cultural events. Relevant here is the pioneering work by Douglass North (1994), who generalized path dependence analysis making it the basis of a theory of institutional change. North's translation of the path dependence thesis to institu­tional change associates historical continuity of all kinds with the path dependence conception. The path dependence thesis serves as an explanation for long-term stability of institutions with different degrees of success and for the predominance of technologies and products, the optimality of which is called into question. The arguments primarily turn against economic equilibrium models in which efficiency is achieved in a state of equilibrium. They are also directed against the notion that “perfect” markets ensure efficient institutions (“invisible hand”). The currently very intense discussion of the path dependence concept in the social sciences is par­ticularly influenced by the work of political scientist Paul Pierson (Pierson 2000; Kay 2005; Howlett and Rayner 2006; Schrodt 2006; Attina 2007; Brandt et al. 2011; Schreyogg et al. 2011). As an example, we can quote the research work by Bechtel and Leuffen (2010) forecasting European Union politics using time series analysis. Authors like Bennet (2008), Bhavnani et al. (2008), Rost et al. (2009), Ward et al. (2010), Braha (2012), Schrodt et al., (2013) have used agent-based computational framework for predicting the onset and duration of civil wars as a consequence of the particular dependence between natural resources, ethnicity and politics. These investigations attempt to provide the policy making community with systematic ex ante forecasts of political events and trends (Agami et al., 2008; Schneider et al. 2010).

Visualizing possible futures of humanity is no more a science fiction dream (Kelly and Kelly 2002; Bishop et al.

Anticipating the future is both a social obligation and intellectual challenge that no scientific discipline can escape. In any case, we should ask whether the future of human events will resemble what we know about the past. The use of formal computational and mathematical approaches does not impose such conclusion, because the same causal mechanism can produce different consequences according to the local circumstances. After all, the extrapolated future is just a probabilistic prediction and not a real fact (Tetlock 1999): once a relevant path has been defined between successive events, when looking into the future, the degree of pre­dictability gradually goes down the further we look and uncertainty goes up. In the very short-term, predictability may be high and forecasting is the working planning mode of choice. In the very long term, everything is uncertain and attempts to planning demonstrate diminishing returns. In the middle zone, there is a level of predictability nut, considerable uncertainty scenarios (Kaivo-oja et al. 2004). Whatever the specific definition, the common denominator of any kind of prediction is a reference to the future. This implies that all sources of uncertainty associated with describing present and past must also be associated with forecasting —and one more: the specification error inherent in the future dimension. In par­ticular, this error should be associated with the distinction between causality and correlation, i.e., the understanding of behavior, the necessary prerequisite for pre­diction. Thus, the key representational problem, the gap between model and reality, and the conditions for controlling that gap, becomes particularly evident in fore­casting (Strand 1999).

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