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AbstractHuman decision-making is affected by a diversity of factors including material cost–benefit considerations, normative and cultural influences, learning and conformity with peers and external authorities (e.g. cultural, religious, political, organisational). Also important are dynamically changing personal perceptions of the situation and beliefs about actions and expectations of others as well as psychological phenomena such as cognitive dissonance and social projection. To better understand these processes, I develop a unifying modelling framework describing the joint dynamics of actions and attitudes of individuals and their beliefs about the actions and attitudes of their groupmates. I consider which norms get internalised and which factors control beliefs about others. I predict that the long-term average characteristics of groups are largely determined by a balance between material payoffs and the values promoted by the external authority. Variation around these averages largely reflects variation in individual costs and benefits mediated by individual psychological characteristics. The efforts of an external authority to change the group behaviour in a certain direction can, counter-intuitively, have an opposite effect on individual behaviour. I consider how various factors can affect differences between groups and societies in the tightness/looseness of their social norms. I show that the most important factors are social heterogeneity, societal threat, effects of authority, cultural variation in the degree of collectivism/individualism, the population size and the subsistence style. My results can be useful for achieving a better understanding of human social behaviour and historical and current social processes, and in developing more efficient policies aiming to modify social behaviour.

Abstract

One of the greatest puzzles of human evolutionary history concerns the how and why of the transition from small-scale, 'simple' societies to large-scale, hierarchically complex ones. This paper reviews theoretical approaches to resolving this puzzle. Our discussion integrates ideas and concepts from evolutionary biology, anthropology, and political science. The evolutionary framework of multilevel selection suggests that complex hierarchies can arise in response to selection imposed by intergroup conflict (warfare). The logical coherency of this theory has been investigated with mathematical models, and its predictions were tested empirically by constructing a database of the largest territorial states in the world (with the focus on the preindustrial era).

AbstractHuman decision-making is controlled by various factors including material cost–benefit considerations, values and beliefs, social influences, cognitive factors and errors. Among social influences, those by external authorities (e.g. educational, cultural, religious, political, administrative, etc.) are particularly important owing to their potential reach and power. To better understand the effects of 'soft' power of authorities we develop a unifying theoretical framework integrating material, cognitive and social forces controlling the joint dynamics of individual actions and beliefs. We apply our approach to three different phenomena: evolution of food sharing in small-scale societies, participation in political protests and effects of priming social identity in behavioural experiments. For each of these applications, we show that our approach leads to different (or simpler) explanations of human behaviour than alternatives. We highlight the type of measurements which can be helpful in developing practical applications of our approach. We identify and explicitly characterise the degree of mismatch between individual actions and attitudes. We assert that the effects of external authorities, of changing beliefs and of differences between people must be studied empirically, included in mathematical models, and accounted for when developing different policies aiming to modify or sustain human behaviour.

Most human societies are characterized by the presence of different identity groups which cooperate but also compete for resources and power. To deepen our understanding of the underlying social dynamics, we model a society subdivided into groups with constant sizes and dynamically changing powers. Both individuals within groups and groups themselves participate in collective actions. The groups are also engaged in political contests over power which determines how jointly produced resources are divided. Using analytical approximations and agent-based simulations, we show that the model exhibits rich behavior characterized by multiple stable equilibria and, under some conditions, non-equilibrium dynamics. We demonstrate that societies in which individuals act independently are more stable than those in which actions of individuals are completely synchronized. We show that mechanisms preventing politically powerful groups from bending the rules of competition in their favor play a key role in promoting between-group cooperation and reducing inequality between groups. We also show that small groups can be more successful in competition than large groups if the jointly-produced goods are rivalrous and the potential benefit of cooperation is relatively small. Otherwise large groups dominate. Overall our model contributes towards a better understanding of the causes of variation between societies in terms of the economic and political inequality within them.

Three predictive problems bedevil our ability to foresee political crises and state breakdown: (1) how to tell when a previously stable state falls into a situation of hidden but dangerous instability; (2) how to tell, once a certain level of instability has appeared in the form of protests, riots, or regional rebellions, whether chaos will grow and accelerate into revolution or civil war, or if the protests are likely to be contained and dampen out; and (3) how to tell which individuals and groups are likely to be the main source of mobilization for radical movements, and whether opposition networks will link up, grow and spread, or be isolated and contained. Prior work has focused on each of these problems separately. However, all three issues are crucial to understanding and foreseeing conflict dynamics. These issues operate on different time-scales and require separate models. In this article we discuss how better models of each process could be developed and, crucially, integrated with data for a more effective prediction system. A major theoretical challenge for us is to link these different approaches in order to increase their predictive power. A major empirical challenge is to identify data (direct or proxy) that can be used to parameterize, validate, and test our models.

When group cohesion is essential, groups must have efficient strategies in place for consensus decision-making. Recent theoretical work suggests that shared decision-making is often the most efficient way for dealing with both information uncertainty and individual variation in preferences. However, some animal and most human groups make collective decisions through particular individuals, leaders, that have a disproportionate influence on group decision-making. To address this discrepancy between theory and data, we study a simple, but general, model that explicitly focuses on the dynamics of consensus building in groups composed by individuals who are heterogeneous in preferences, certain personality traits (agreeability and persuasiveness), reputation, and social networks. We show that within-group heterogeneity can significantly delay democratic consensus building as well as give rise to the emergence of informal leaders, i.e. individuals with a disproportionately large impact on group decisions. Our results thus imply strong benefits of leadership particularly when groups experience time pressure and significant conflict of interest between members (due to various between-individual differences). Overall, our models shed light on why leadership and decision-making hierarchies are widespread, especially in human groups.

Warfare is commonly viewed as a driving force of the process of aggregation of initially independent villages into larger and more complex political units that started several thousand years ago and quickly lead to the appearance of chiefdoms, states, and empires. Here we build on extensions and generalizations of Carneiro's (1970) argument to develop a spatially explicit agent-based model of the emergence of early complex societies via warfare. In our model polities are represented as hierarchically structured networks of villages whose size, power, and complexity change as a result of conquest, secession, internal reorganization (via promotion and linearization), and resource dynamics. A general prediction of our model is continuous stochastic cycling in which the growth of individual polities in size, wealth/power, and complexity is interrupted by their quick collapse. The model dynamics are mostly controlled by two parameters, one of which scales the relative advantage of wealthier polities in between and within-polity conflicts, and the other is the chief's expected time in power. Our results demonstrate that the stability of large and complex polities is strongly promoted if the outcomes of the conflicts are mostly determined by the polities' wealth/power, if there exist well-defined and accepted means of succession, and if control mechanisms are internally specialized.

AbstractDynamic norms have recently emerged as a powerful method to encourage individuals to adopt an innovation by highlighting a growing trend in its uptake. However, there have been no concrete attempts to understand how this individual-level mechanism might shape the collective population behavior. Here, we develop a framework to examine this by encapsulating dynamic norms within a game-theoretic mathematical model for innovation diffusion. Specifically, we extend a network coordination game by incorporating a probabilistic mechanism where an individual adopts the action with growing popularity, instead of the standard best-response update rule; the probability of such an event captures the population's "sensitivity" to dynamic norms. Theoretical analysis reveals that sensitivity to dynamic norms is key to facilitating social diffusion. Small increases in sensitivity reduces the advantage of the innovation over status quo or the number of initial innovators required to unlock diffusion, while a sufficiently large sensitivity alone guarantees diffusion.

Abstract
We consider complex multistage multiagent negotiation processes such as those occurring at climate conferences and ask ourselves how can an agent maximize its social power, intended as influence over the outcome of the negotiation. This question can be framed as a strategic game played over an opinion dynamics model, in which the action of an agent consists in stubbornly defending its own opinion. We show that for consensus-seeking opinion dynamics models in which the interaction weights are uniform, the optimal action obeys to an early mover advantage principle, i.e. the agents behaving stubbornly in the early phases of the negotiations achieve the highest social power. When looking at data collected from the climate change negotiations going on at the United Nations Framework Convention on Climate Change, we find evidence of the use of the early mover strategy. Furthermore, we show that the social powers computed through our model correlate very well with the perceived leadership roles assessed through independent survey data, especially when non-uniform weights incorporating economical and demographic factors are considered.

Abstract

How did human societies evolve from small groups, integrated by face-to-face cooperation, to huge anonymous societies of today? Why is there so much variation in the ability of different human populations to construct viable states? We developed a model that uses cultural evolution mechanisms to predict where and when the largest-scale complex societies should have arisen in human history. The model was simulated within a realistic landscape of the Afroeurasian landmass, and its predictions were tested against real data. Overall, the model did an excellent job predicting empirical patterns. Our results suggest a possible explanation as to why a long history of statehood is positively correlated with political stability, institutional quality, and income per capita.

Abstract
Past major epidemic events showed that when an infectious disease is perceived to cause severe health outcomes, individuals modify health behavior affecting epidemic dynamics. To investigate the effect of this feedback relationship on epidemic dynamics, we developed a compartmental model that couples a disease spread framework with competition of two mutually exclusive health opinions (health-positive and health-neutral) associated with different health behaviors. The model is based on the assumption that individuals switch health opinions as a result of exposure to opinions of others through interpersonal communications. To model opinion switch rates, we considered a family of functions and identified the ones that allow health opinions to coexist. Finally, the model includes assortative mixing by opinions. In the disease-free population, either the opinions cannot coexist and one of them is always dominating (mono-opinion equilibrium) or there is at least one stable coexistence of opinions equilibrium. In the latter case, there is multistability between the coexistence equilibrium and the two mono-opinion equilibria. When two opinions coexist, it depends on their distribution whether the infection can invade. If presence of the infection leads to increased switching to a health-positive opinion, the epidemic burden becomes smaller than indicated by the basic reproduction number. Additionally, a feedback between epidemic dynamics and health opinion dynamics may result in (sustained) oscillatory dynamics and a switch to a different stable opinion distribution. Our model captures feedback between spread of awareness through social interactions and infection dynamics and can serve as a basis for more elaborate individual-based models.

Recent years have seen major political crises throughout the world, and foreign policy analysts nearly universally expect to see rising tensions within (and between) countries in the next 5–20 years. Being able to predict future crises and to assess the resilience of different countries to various shocks is of foremost importance in averting the potentially huge human costs of state collapse and civil war. The premise of this paper is that a transdisciplinary approach to forecasting social breakdown, recovery, and resilience is entirely feasible, as a result of recent breakthroughs in statistical analysis of large-scale historical data, the qualitative insights of historical and semiotic investigations, and agent-based models that translate between micro-dynamics of interacting individuals and the collective macro-level events emerging from these interactions. Our goal is to construct a series of probabilistic scenarios of social breakdown and recovery, based on historical crises and outcomes, which can aid the analysis of potential outcomes of future crises. We call this approach—similar in spirit to ensemble forecasting in weather prediction—multipath forecasting (MPF). This paper aims to set out the methodological premises and basic stages envisaged to realize this goal within a transdisciplinary research collaboration: first, the statistical analysis of a massive database of past instances of crisis to determine how actual outcomes (the severity of disruption and violence, the speed of resolution) depend on inputs (economic, political, and cultural factors); second, the encoding of these analytical insights into probabilistic, empirically informed computational models of societal breakdown and recovery—the MPF engine; third, testing the MPF engine to "predict" the trajectories and outcomes of another set of past social upheavals, which were not used in building the model. This "historical retrodiction" is an innovation that will allow us to further refine the MPF technology. Ultimately our vision is to use MPF to help write what we call "a history of possible futures," in which the near- and medium-term paths of societies are probabilistically forecast. ; A History of Possible Futures: Multipath Forecasting of Social breakdown, Recovery and Resilience ; publishedVersion