Harmen de Weerd

How much does it help to know what she knows you know? an agent-based simulation study

In everyday life, people make use of theory of mind by explicitly attributing unobservable mental content such as beliefs, desires, and intentions to others. Humans are known to be able to use this ability recursively. That is, they engage in higher-order theory of mind, and consider what others believe about their own beliefs. In this paper, we use agent-based computational models to investigate the evolution of higher-order theory of mind. We consider higher-order theory of mind across four different competitive games, including repeated single-shot and repeated extensive form games, and determine the advantage of higher-order theory of mind agents over their lower-order theory of mind opponents. Across these four games, we find a common pattern in which first-order and second-order theory of mind agents clearly outperform opponents that are more limited in their ability to make use of theory of mind, while the advantage for deeper recursion to third-order theory of mind is limited in comparison.

Evolution of altruistic punishment in heterogeneous populations.

Evolutionary models for altruistic behavior typically make the assumption of homogeneity: each individual has the same costs and benefits associated with cooperating with each other and punishing for selfish behavior. In this paper, we relax this assumption by separating the population into heterogeneous classes, such that individuals from different classes differ in their ability to punish for selfishness. We compare the effects of introducing heterogeneity this way across two population models, that each represents a different type of population: the infinite and well-mixed population describes the way workers of social insects such as ants are organized, while a spatially structured population is more related to the way social norms evolve and are maintained in a social network. We find that heterogeneity in the effectiveness of punishment by itself has little to no effect on whether or not altruistic behavior will stabilize in a population. In contrast, heterogeneity in the cost that individuals pay to punish for selfish behavior allows altruistic behavior to be maintained more easily. Fewer punishers are needed to deter selfish behavior, and the individuals that punish will mostly belong to the class that pays a lower cost to do so. This effect is amplified when individuals that pay a lower cost for punishing inflict a higher punishment. The two population models differ when individuals that pay a low cost for punishing also inflict a lower punishment. In this situation, altruistic behavior becomes harder to maintain in an infinite and well-mixed population. However, this effect does not occur when the population is spatially structured.

A Novel Mechanism for a Survival Advantage of Vigilant Individuals in Groups

In many animal species, vigilance is crucial for avoiding predation. In groups, however, nonvigilant individuals could benefit from the vigilance of others without any of the associated costs. In an evolutionary sense, such exploitation may be compensated if vigilant individuals have a survival advantage. The novelty in our model is that the probability to detect a predator is “distance dependent.” We show that even if nonvigilant individuals benefit fully from information produced by vigilant individuals, vigilant individuals nevertheless enjoy a survival advantage. This happens because detection of predators is more likely when vigilant individuals happen to be targets of predation. We expect this distance-dependent mechanism to be compatible with previously reported mechanisms.

Negotiating with other minds: the role of recursive theory of mind in negotiation with incomplete information

Theory of mind refers to the ability to reason explicitly about unobservable mental content of others, such as beliefs, goals, and intentions. People often use this ability to understand the behavior of others as well as to predict future behavior. People even take this ability a step further, and use higher-order theory of mind by reasoning about the way others make use of theory of mind and in turn attribute mental states to different agents. One of the possible explanations for the emergence of the cognitively demanding ability of higher-order theory of mind suggests that it is needed to deal with mixed-motive situations. Such mixed-motive situations involve partially overlapping goals, so that both cooperation and competition play a role. In this paper, we consider a particular mixed-motive situation known as Colored Trails, in which computational agents negotiate using alternating offers with incomplete information about the preferences of their trading partner. In this setting, we determine to what extent higher-order theory of mind is beneficial to computational agents. Our results show limited effectiveness of first-order theory of mind, while second-order theory of mind turns out to benefit agents greatly by allowing them to reason about the way they can communicate their interests. Additionally, we let human participants negotiate with computational agents of different orders of theory of mind. These experiments show that people spontaneously make use of second-order theory of mind in negotiations when their trading partner is capable of second-order theory of mind as well.

Agent-Based Models for Higher-Order Theory of Mind

Agent-based models are a powerful tool for explaining the emergence of social phenomena in a society. In such models, individual agents typically have little cognitive ability. In this paper, we model agents with the cognitive ability to make use of theory of mind. People use this ability to reason explicitly about the beliefs, desires, and goals of others. They also take this ability further, and expect other people to have access to theory of mind as well. To explain the emergence of this higher-order theory of mind, we place agents capable of theory of mind in a particular negotiation game known as Colored Trails, and determine to what extent theory of mind is beneficial to computational agents. Our results show that the use of first-order theory of mind helps agents to offer better trades. We also find that second-order theory of mind allows agents to perform better than first-order colleagues, by taking into account competing offers that other agents may make. Our results suggest that agents experience diminishing returns on orders of theory of mind higher than level two, similar to what is seen in people. These findings corroborate those in more abstract settings.

Estimating the Use of Higher-Order Theory of Mind Using Computational Agents

Abstract When people make decisions in a social context, they often make use of theory of mind, by reasoning about unobservable mental content of others. For example, the behavior of a pedestrian who wants to cross the street depends on whether or not he believes that the driver of an oncoming car has seen him or not. People can also reason about the theory of mind abilities of others, leading to recursive thinking of the sort ‘I think that you think that I think…’. Previous research suggests that this ability may be especially effective in simple competitive settings. In this paper, we use a combination of computational agents and Bayesian model selection to determine to what extent people make use of higher-order theory of mind reasoning in a particular competitive game known as matching pennies. We find that while many children and adults appear to make use of theory of mind, participants are also often classified as using a simpler reactive strategy based only on the actions of the directly preceding round. This may indicate that human reasoners do not primarily use their theory of mind abilities to compete with others.

The Origin of Mimicry

One of the most remarkable phenomena in nature is mimicry, in which one species (the mimic) evolves to imitate the phenotype of another species (the model). Several reasons for the origin of mimicry have been proposed, but no definitive conclusion has been found yet. In this paper, we test several of these hypotheses through an agent based co-evolutionary model. In particular, we consider two possible alternatives: (1) Deception, in which mimics evolve to imitate the phenotype of models that predators avoid to eat, and (2) Coincidence, in which models evolve a warning color to avoid predation, which coincidentally benefits the mimics. Our agent-based simulation shows that both these hypotheses are plausible origins for mimicry, but also that once a mimicry situation has been established through coincidence, mimics will take advantage of the possibility for deception as well.