The contemporary global community is increasingly interdependent and confronted with systemic risks posed by the actions and interactions of actors existing beneath the level of formal institutions, often operating outside effective governance structures. Frequently, these actors are human agents, such as rogue traders or aggressive financial innovators, terrorists, groups of dissidents, or unauthorized sources of sensitive or secret information about government or private sector activities. In other instances, influential "actors" take the form of climate change, communications technologies, or socioeconomic globalization. Although these individual forces may be small relative to state governments or international institutions, or may operate on long time scales, the changes they catalyze can pose significant challenges to the analysis and practice of international relations through the operation of complex feedbacks and interactions of individual agents and interconnected systems. We call these challenges "femtorisks," and emphasize their importance for two reasons. First, in isolation, they may be inconsequential and semiautonomous; but when embedded in complex adaptive systems, characterized by individual agents able to change, learn from experience, and pursue their own agendas, the strategic interaction between actors can propel systems down paths of increasing, even global, instability. Second, because their influence stems from complex interactions at interfaces of multiple systems (e.g., social, financial, political, technological, ecological, etc.), femtorisks challenge standard approaches to risk assessment, as higher-order consequences cascade across the boundaries of socially constructed complex systems. We argue that new approaches to assessing and managing systemic risk in international relations are required, inspired by principles of evolutionary theory and development of resilient ecological systems.complex adaptive systems | systemic risk | risk analysis | contagion | resilience
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In this essay we make four interrelated points. First, we reiterate previous arguments (Kleinberg et al 2015) that forecasting problems are more common in social science than is often appreciated. From this observation it follows that social scientists should care about predictive accuracy in addition to unbiased or consistent estimation of causal relationships. Second, we argue that social scientists should be interested in prediction even if they have no interest in forecasting per se. Whether they do so explicitly or not, that is, causal claims necessarily make predictions; thus it is both fair and arguably useful to hold them accountable for the accuracy of the predictions they make. Third, we argue that prediction, used in either of the above two senses, is a useful metric for quantifying progress. Important differences between social science explanations and machine learning algorithms notwithstanding, social scientists can still learn from approaches like the Common Task Framework (CTF) which have successfully driven progress in certain fields of AI over the past 30 years (Donoho, 2015). Finally, we anticipate that as the predictive performance of forecasting models and explanations alike receives more attention, it will become clear that it is subject to some upper limit which lies well below deterministic accuracy for many applications of interest (Martin et al 2016). Characterizing the properties of complex social systems that lead to higher or lower predictive limits therefore poses an interesting challenge for computational social science.
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