The Evolution of Language Groups among Cooperating Digital Predators

Many species of animals have evolved complex means for communicating with one another. Oftentimes, communication is essential for the execution of tasks that require cooperation between individuals, such as group hunting and mate selection. As a result, communication itself becomes essential for survival. While these facts are readily observed, the evolutionary processes underlying them are less understood, in large part because observational - much less controlled - studies of these processes are impossible. Both the timescales and population sizes required for such studies are simply too great. To address these problems, this thesis uses simulated predators to study the evolution of language in animals. These digital predators evolve to perform two cooperative tasks: hunting and mate selection. After the populations of predators have evolved to perform both tasks successfully, the population is decomposed into both language groups and cooperative groups. Spectral clustering identifies predators that speak similar languages, while merge clustering is used to find those groups of predators that are the most successful when working together. Analysis of the groups generated by these two different methods shows that the most successful pairings are not necessarily those in which the two individuals are speaking the same language. Rather, organisms can evolve to speak a different language than the one to which they respond. Moreover, even though one task -- mate selection -- evolves earlier in evolutionary history, the language diversity it produces counteracts any head-start provided for the evolution of the second task. Thus, not only is language important for the evolution of cooperative task success, but the appearance of language groups can also play a determinant role in the evolution of cooperation.Computer Science

Democracy under uncertainty: The ‘wisdom of crowds’ and the free-rider problem in group decision making

We introduce a game theory model of individual decisions to cooperate by contributing personal resources to group decisions versus by free-riding on the contributions of other members. In contrast to most public-goods games that assume group returns are linear in individual contributions, the present model assumes decreasing marginal group production as a function of aggregate individual contributions. This diminishing marginal returns assumption is more realistic and generates starkly different predictions compared to the linear model. One important implication is that, under most conditions, there exist equilibria where some, but not all members of a group contribute, even with completely self-interested motives. An agent-based simulation confirms the individual and group advantages of the equilibria in which behavioral asymmetry emerges from a game structure that is a priori perfectly symmetric for all agents (all agents have the same payoff function and action space, but take different actions in equilibria). And a behavioral experiment demonstrates that cooperators and free-riders coexist in a stable manner in groups performing with the non-linear production function. A collateral result demonstrates that, compared to a ―dictatorial‖ decision scheme guided by the best member in a group, the majority-plurality decision rules can pool information effectively and produce greater individual net welfare at equilibrium, even if free-riding is not sanctioned. This is an original proof that cooperation in ad hoc decision-making groups can be understood in terms of self-interested motivations and that, despite the free-rider problem, majority-plurality decision rules can function robustly as simple, efficient social decision heuristics.group decision making under uncertainty, free-rider problem, majority-plurality rules, marginally-diminishing group returns, evolutionary games, behavioral experiment

Decoding social intentions in human prehensile actions: Insights from a combined kinematics-fMRI study

Consistent evidence suggests that the way we reach and grasp an object is modulated not only by object properties (e.g., size, shape, texture, fragility and weight), but also by the types of intention driving the action, among which the intention to interact with another agent (i.e., social intention). Action observation studies ascribe the neural substrate of this `intentional' component to the putative mirror neuron (pMNS) and the mentalizing (MS) systems. How social intentions are translated into executed actions, however, has yet to be addressed. We conducted a kinematic and a functional Magnetic Resonance Imaging (fMRI) study considering a reach-to-grasp movement performed towards the same object positioned at the same location but with different intentions: passing it to another person (social condition) or putting it on a concave base (individual condition). Kinematics showed that individual and social intentions are characterized by different profiles, with a slower movement at the level of both the reaching (i.e., arm movement) and the grasping (i.e., hand aperture) components. fMRI results showed that: (i) distinct voxel pattern activity for the social and the individual condition are present within the pMNS and the MS during action execution; (ii) decoding accuracies of regions belonging to the pMNS and the MS are correlated, suggesting that these two systems could interact for the generation of appropriate motor commands. Results are discussed in terms of motor simulation and inferential processes as part of a hierarchical generative model for action intention understanding and generation of appropriate motor commands

Autistic traits affect interpersonal motor coordination by modulating strategic use of role-based behavior

Background: Despite the fact that deficits in social communication and interaction are at the core of Autism Spectrum Conditions (ASC), no study has yet tested individuals on a continuum from neurotypical development to autism in an on-line, cooperative, joint action task. In our study, we aimed to assess whether the degree of autistic traits affects participants' ability to modulate their motor behavior while interacting in a Joint Grasping task and according to their given role. Methods: Sixteen pairs of adult participants played a cooperative social interactive game in which they had to synchronize their reach-to-grasp movements. Pairs were comprised of one ASC and one neurotypical with no cognitive disability. In alternate experimental blocks, one participant knew what action to perform (instructed role) while the other had to infer it from his/her partner’s action (adaptive role). When in the adaptive condition, participants were told to respond with an action that was either opposite or similar to their partner. Participants also played a non-social control game in which they had to synchronize with a non-biological stimulus. Results: In the social interactive task, higher degree of autistic trait s predicted less ability to mod ulate joint action according to one’s interactive role. In the non-social task, autistic traits did not predict differences in movement preparation and planning, thus ruling out the possibility that social interact ive task results were due to basic motor or executive function difficulties. Furthermore, when participants played the non-social game, the higher their autistic traits, the more they were interfered by the non-biological stimulus. Conclusions: Our study shows for the first time that high autistic traits predict a stereotypical interaction style when individuals are required to modulate their movements in order to coordinate with their partner according to their role in a joint action task. Specifically, the infrequent emergence of role-based motor behavior modulation during on-line motor cooperation in participants with high autistic traits sheds light on the numerous difficulties ASC have in nonverbal social interaction

Understanding collaboration in volunteer computing systems

Volunteer computing is a paradigm in which devices participating in a distributed environment share part of their resources to help others perform their activities. The effectiveness of this computing paradigm depends on the collaboration attitude adopted by the participating devices. Unfortunately for software designers it is not clear how to contribute with local resources to the shared environment without compromising resources that could then be required by the contributors. Therefore, many designers adopt a conservative position when defining the collaboration strategy to be embedded in volunteer computing applications. This position produces an underutilization of the devices’ local resources and reduces the effectiveness of these solutions. This article presents a study that helps designers understand the impact of adopting a particular collaboration attitude to contribute with local resources to the distributed shared environment. The study considers five collaboration strategies, which are analyzed in computing environments with both, abundance and scarcity of resources. The obtained results indicate that collaboration strategies based on effort-based incentives work better than those using contribution-based incentives. These results also show that the use of effort-based incentives does not jeopardize the availability of local resources for the local needs.Peer ReviewedPostprint (published version

Cognitive modeling of social behaviors

To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual mind as ways of carrying out activities. This requires for the psychologist a shift from only modeling goals and tasks —why people do what they do—to modeling behavioral patterns—what people do—as they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts). To illustrate these ideas, this article presents an extract from a Brahms simulation of the Flashline Mars Arctic Research Station (FMARS), in which a crew of six people are living and working for a week, physically simulating a Mars surface mission. The example focuses on the simulation of a planning meeting, showing how physiological constraints (e.g., hunger, fatigue), facilities (e.g., the habitat’s layout) and group decision making interact. Methods are described for constructing such a model of practice, from video and first-hand observation, and how this modeling approach changes how one relates goals, knowledge, and cognitive architecture. The resulting simulation model is a powerful complement to task analysis and knowledge-based simulations of reasoning, with many practical applications for work system design, operations management, and training

Cooperation and Contagion in Web-Based, Networked Public Goods Experiments

A longstanding idea in the literature on human cooperation is that cooperation should be reinforced when conditional cooperators are more likely to interact. In the context of social networks, this idea implies that cooperation should fare better in highly clustered networks such as cliques than in networks with low clustering such as random networks. To test this hypothesis, we conducted a series of web-based experiments, in which 24 individuals played a local public goods game arranged on one of five network topologies that varied between disconnected cliques and a random regular graph. In contrast with previous theoretical work, we found that network topology had no significant effect on average contributions. This result implies either that individuals are not conditional cooperators, or else that cooperation does not benefit from positive reinforcement between connected neighbors. We then tested both of these possibilities in two subsequent series of experiments in which artificial seed players were introduced, making either full or zero contributions. First, we found that although players did generally behave like conditional cooperators, they were as likely to decrease their contributions in response to low contributing neighbors as they were to increase their contributions in response to high contributing neighbors. Second, we found that positive effects of cooperation were contagious only to direct neighbors in the network. In total we report on 113 human subjects experiments, highlighting the speed, flexibility, and cost-effectiveness of web-based experiments over those conducted in physical labs

The role of simulations in consumer experiences and behavior: insights from the grounded cognition theory of desire

What are the mechanisms by which extrinsic and environmental cues affect consumer experiences, desires, and choices? Based on the recent grounded cognition theory of desire, we argue that consumption and reward simulations constitute a central mechanism in these phenomena. Specifically, we argue that appetitive stimuli, such as specific product cues, can activate simulations of consuming and enjoying the respective products, based on previous learning experiences. These consumption and reward simulations can lead to motivated behavior, and can be modulated by state and trait individual differences, situational factors, and product-extrinsic cues. We outline the role of simulations within the grounded theory of desire, offering a theoretical framework for understanding motivational processes in consumer behavior. Then we illustrate the theory with behavioral, physiological, and neuroimaging findings on simulations in appetitive behavior and sensory marketing. Finally, we outline important issues for further research and applications for stimulating healthy, prosocial, and sustainable consumer choices