Value homophily benefits cooperation but motivates employing incorrect social information.

Individuals often judge others based on third-party gossip, rather than their own experience, despite the fact that gossip is error-prone. Rather than judging others on their merits, even when such knowledge is free, we judge based on the opinions of third parties. Here we seek to understand this observation in the context of the evolution of cooperation. If individuals are being judged on noisy social reputations rather than on merit, then agents might exploit this, eroding the sustainability of cooperation. We employ a version of the Prisoner׳s Dilemma, the Donation game, which has been used to simulate the evolution of cooperation through indirect reciprocity. First, we validate the proposition that adding homophily (the propensity to interact with others of similar beliefs) into a society increases the sustainability of cooperation. However, this creates an evolutionary conflict between the accurate signalling of ingroup status versus the veridical report of the behaviour of other agents. We find that conditions exist where signalling ingroup status outweighs honesty as the best method to ultimately spread cooperation.

Replicators, lineages, and interactors.

The target article argues that whole groups can act as interactors in an evolutionary process. We believe that Smaldino's discussion would be advanced by a more thorough analysis of the appropriate replicators and lineages for this model. We show that cultural evolution is necessarily a separate process from cultural group selection, and we also illustrate that the two processes may influence each other as demonstrated by an agent-based model of communicating food-processing skills.

Do We Collaborate With What We Design?

The use of terms like "collaboration" and "co-workers" to describe interactions between human beings and certain artificial intelligence (AI) systems has gained significant traction in recent years. Yet, it remains an open question whether such anthropomorphic metaphors provide either a fertile or even a purely innocuous lens through which to conceptualize designed commercial products. Rather, a respect for human dignity and the principle of transparency may require us to draw a sharp distinction between real and faux peers. At the heart of the concept of collaboration lies the assumption that the collaborating parties are (or behave as if they are) of similar status: two agents capable of comparable forms of intentional action, moral agency, or moral responsibility. In application to current AI systems, this not only seems to fail ontologically but also from a socio-political perspective. AI in the workplace is primarily an extension of capital, not of labor, and the AI "co-workers" of most individuals will likely be owned and operated by their employer. In this paper, we critically assess both the accuracy and desirability of using the term "collaboration" to describe interactions between humans and AI systems. We begin by proposing an alternative ontology of human-machine interaction, one which features not two equivalently autonomous agents, but rather one machine that exists in a relationship of heteronomy to one or more human agents. In this sense, while the machine may have a significant degree of independence concerning the means by which it achieves its ends, the ends themselves are always chosen by at least one human agent, whose interests may differ from those of the individuals interacting with the machine. We finally consider the motivations and risks inherent to the continued use of the term "collaboration," exploring its strained relation to the concept of transparency, and consequences for the future of work.

Punishment can promote defection in group-structured populations.

Pro-social punishment, whereby cooperators punish defectors, is often suggested as a mechanism that maintains cooperation in large human groups. Importantly, models that support this idea have to date only allowed defectors to be the target of punishment. However, recent empirical work has demonstrated the existence of anti-social punishment in public goods games. That is, individuals that defect have been found to also punish cooperators. Some recent theoretical studies have found that such anti-social punishment can prevent the evolution of pro-social punishment and cooperation. However, the evolution of anti-social punishment in group-structured populations has not been formally addressed. Previous work has informally argued that group-structure must favour pro-social punishment. Here we formally investigate how two demographic factors, group size and dispersal frequency, affect selection pressures on pro- and anti-social punishment. Contrary to the suggestions of previous work, we find that anti-social punishment can prevent the evolution of pro-social punishment and cooperation under a range of group structures. Given that anti-social punishment has now been found in all studied extant human cultures, the claims of previous models showing the co-evolution of pro-social punishment and cooperation in group-structured populations should be re-evaluated.

Polarization under rising inequality and economic decline.

Social and political polarization is an important source of conflict in many societies. Understanding its causes has become a priority of scholars across disciplines. We demonstrate that shifts in socialization strategies analogous to political polarization can arise as a locally beneficial response to both rising wealth inequality and economic decline. In many contexts, interaction with diverse out-groups confers benefits from innovation and exploration greater than those that arise from interacting exclusively with a homogeneous in-group. However, when the economic environment favors risk aversion, a strategy of seeking lower-risk in-group interactions can be important to maintaining individual solvency. Our model shows that under conditions of economic decline or increasing inequality, some members of the population benefit from adopting a risk-averse, in-group favoring strategy. Moreover, we show that such in-group polarization can spread rapidly to the whole population and persist even when the conditions that produced it have reversed.

Skill acquisition through program-level imitation in a real-time domain.

This paper presents an imitation learning system capable of learning tasks in a complex dynamic real-time environment. In this paper, we argue that social learning should be thought of as a special case of general skill learning, and that the biases it presents to the skill learning problem radically simplify learning for species with sufficient innate predisposition to harness this power. We decompose skill learning into four subproblems, then show how a modification of Roy's CELL system can address all these problems simultaneously. Our system is demonstrated working in the domain of a real-time virtual-reality game, Unreal Tournament.

Semantics derived automatically from language corpora contain human-like biases.

Machine learning is a means to derive artificial intelligence by discovering patterns in existing data. Here, we show that applying machine learning to ordinary human language results in human-like semantic biases. We replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web. Our results indicate that text corpora contain recoverable and accurate imprints of our historic biases, whether morally neutral as toward insects or flowers, problematic as toward race or gender, or even simply veridical, reflecting the status quo distribution of gender with respect to careers or first names. Our methods hold promise for identifying and addressing sources of bias in culture, including technology.

Primate errors in transitive 'inference': a two-tier learning model.

Transitive performance (TP) is a learning-based behaviour exhibited by a wide range of species, where if a subject has been taught to prefer A when presented with the pair AB but to prefer B when presented with the pair BC, then the subject will also prefer A when presented with the novel pair AC. Most explanations of TP assume that subjects recognize and learn an underlying sequence from observing the training pairs. However, data from squirrel monkeys (Saimiri sciureus) and young children contradict this, showing that when three different items (a triad) are drawn from the sequence, subjects' performance degrades systematically (McGonigle and Chalmers, Nature 267:694-696, 1977; Chalmers and McGonigle, Journal of Experimental Child Psychology 37:355-377, 1984; Harris and McGonigle, The Quarterly Journal of Experimental Psychology 47B:319-348, 1994). We present here the two-tier model, the first learning model of TP which accounts for this systematic performance degradation. Our model assumes primate TP is based on a general-purpose task learning system rather than a special-purpose sequence-learning system. It supports the hypothesis of Heckers et al. (Hippocampus 14:153-162, 2004) that TP is an expression of two separate general learning elements: one for associating actions and contexts, another for prioritising associations when more than one context is present. The two-tier model also provides explanations for why phased training is important for helping subjects learn the initial training pairs and why some subjects fail to do so. It also supports the Harris and McGonigle (The Quarterly Journal of Experimental Psychology 47B:319-348, 1994) explanation of why, once the training pairs have been acquired, subjects perform transitive choice automatically on two-item diads, but not when exposed to triads from the same sequence.

Agent-based modelling as scientific method: a case study analysing primate social behaviour.

A scientific methodology in general should provide two things: first, a means of explanation and, second, a mechanism for improving that explanation. Agent-based modelling (ABM) is a method that facilitates exploring the collective effects of individual action selection. The explanatory force of the model is the extent to which an observed meta-level phenomenon can be accounted for by the behaviour of its micro-level actors. This article demonstrates that this methodology can be applied to the biological sciences; agent-based models, like any other scientific hypotheses, can be tested, critiqued, generalized or specified. We review the state of the art for ABM as a methodology for biology and then present a case study based on the most widely published agent-based model in the biological sciences: Hemelrijk's DomWorld, a model of primate social behaviour. Our analysis shows some significant discrepancies between this model and the behaviour of the macaques, the genus used for our analysis. We also demonstrate that the model is not fragile: its other results are still valid and can be extended to compensate for these problems. This robustness is a standard advantage of experiment-based artificial intelligence modelling techniques over analytic modelling.

Introduction. Modelling natural action selection.

Action selection is the task of resolving conflicts between competing behavioural alternatives. This theme issue is dedicated to advancing our understanding of the behavioural patterns and neural substrates supporting action selection in animals, including humans. The scope of problems investigated includes: (i) whether biological action selection is optimal (and, if so, what is optimized), (ii) the neural substrates for action selection in the vertebrate brain, (iii) the role of perceptual selection in decision-making, and (iv) the interaction of group and individual action selection. A second aim of this issue is to advance methodological practice with respect to modelling natural action section. A wide variety of computational modelling techniques are therefore employed ranging from formal mathematical approaches through to computational neuroscience, connectionism and agent-based modelling. The research described has broad implications for both natural and artificial sciences. One example, highlighted here, is its application to medical science where models of the neural substrates for action selection are contributing to the understanding of brain disorders such as Parkinson's disease, schizophrenia and attention deficit/hyperactivity disorder.