Collective computation and the emergence of hunter-gatherer small-worlds

Two defining features of human sociality are large brains with neurally-dense cerebral cortices and the propensity to form complex social networks with non-kin. Large brains and the social networks in which they are embedded facilitate flows of fitness-enhancing information at multiple scales, but are also energetically expensive. In this paper, we consider how flows of energy and information interact to shape the macroscopic features of hunter-gatherer socioecology. Collective computation is the processing of information by complex adaptive systems to generate inferences in order to solve adaptive problems. In hunter-gatherer societies the adaptive problem is how to maximize fitness by optimizing information processing given the energy constraints of complex environments. The solution is the emergent macroscopic structure of the socioecology. Here we show how computation is extended across social networks to form the decentralized knowledge systems characteristic of hunter-gatherer societies. Data show that hunter-gatherer bands of co-residing families constitute computationally powerful networks that are embedded within hierarchically modular social networks that form complex metapopulations bound by fission-fusion dynamics at multiple scales facilitating the flow of information far beyond local interactions. These dynamics lead to the emergence of hunter-gatherer small-worlds where highly clustered local interactions are embedded within much larger, but sparsely connected metapopulations. Hunter-gatherers optimize local energy budgets in small groups but maintain interactions with much larger social networks while avoiding many of the ecological costs.

How to reconnect different small worlds: Deconstructing the past that divides, pursuing a future that unites

Violence associated with religion is prevalent globally. Informed by the concept of small world, in which people learn about beliefs and values to judge what information is relevant to them, this study investigates how information sharing helps a religiously polarized society depolarize after a series of violence. Based on 54 interviews and participant observations in Ambon, Indonesia, this study found that, after the violence ended, deconstructing fear of the other religious community conditioned the Ambonese to rethink the relevance of living in the small world. As one community managed to meaningfully interact with the other, opportunities for exchanging views and rebuilding relationships emerged. Re-establishing common values enabled the disparate communities to unite as a society for the sake of their collective future. These findings broadly offer insights as to how to reconcile differences in competing small worlds, especially where religion is an imminent threat to social cohesion.

An ocean of languages? Multilingualism in Arundhati Roy’s The Ministry of Utmost Happiness

The article examines the multilingual poetics of Arundhati Roy’s novel The Ministry of Utmost Happiness, focusing on the historically strained relations between English and India’s other languages. As a contribution to the project of “unforgetting English” (Walkowitz, 2015), the close reading of Roy’s novel reveals how English is construed and posited as a language within India’s multilingual environment. The use of English in Roy’s novel is tinged with a peculiar ambivalence: while it is marked as an alien language that is inimical to India’s linguistic plurality, English nevertheless serves as a literary and linguistic mediator that makes possible the encounter between different Indian languages in the first place. My article argues that the novel’s uneasy multilingualism navigates the possibilities (and impossibilities) of producing a literature that espouses ex-centric and small worlds in the hyper-central language of English.

Ambiguity aversion, modern Bayesianism and small worlds

The central question of this paper is whether a rational agent under uncertainty can exhibit ambiguity aversion (AA). The answer to this question depends on the way the agent forms her probabilistic beliefs: classical Bayesianism (CB) vs modern Bayesianism (MB). We revisit Schmeidler's coin-based example and show that a rational MB agent operating in the context of a "small world", cannot exhibit AA. Hence we argue that the motivation of AA based on Schmeidler's coin-based and Ellsberg's classic urn-based examples, is poor, since they correspond to cases of "small worlds". We also argue that MB, not only avoids AA, but also proves to be normatively superior to CB because an MB agent (i) avoids logical inconsistencies akin to the relation between her subjective probability and objective chance, (ii) resolves the problem of "old evidence" and (iii) allows psychological detachment from actual evidence, hence avoiding the problem of "cognitive dissonance". As far as AA is concerned, we claim that it may be thought of as a (potential) property of large worlds, because in such worlds MB is likely to be infeasible.

Information Theory for Agents in Artificial Intelligence, Psychology, and Economics

This review covers some of the central relationships between artificial intelligence, psychology, and economics through the lens of information theory, specifically focusing on formal models of decision-theory. In doing so we look at a particular approach that each field has adopted and how information theory has informed the development of the ideas of each field. A key theme is expected utility theory, its connection to information theory, and the Bayesian approach to decision-making and forms of (bounded) rationality. What emerges from this review is a broadly unified formal perspective derived from three very different starting points that reflect the unique principles of each field. Each of the three approaches reviewed can, in principle at least, be implemented in a computational model in such a way that, with sufficient computational power, they could be compared with human abilities in complex tasks. However, a central critique that can be applied to all three approaches was first put forward by Savage in The Foundations of Statistics and recently brought to the fore by the economist Binmore: Bayesian approaches to decision-making work in what Savage called `small worlds’ but cannot work in `large worlds’. This point, in various different guises, is central to some of the current debates about the power of artificial intelligence and its relationship to human-like learning and decision-making. Recent work on artificial intelligence has gone some way to bridging this gap but significant questions still need to be answered in all three fields in order to make progress on these problems.