Mesoscopic Interactions and Species Coexistence in Evolutionary Game Dynamics of Cyclic Competitions

Date of Acceptance: 27/11/2014Peer reviewedPublisher PD

Using artificial intelligence techniques for strategy generation in the Commons game

In this paper, we consider the use of artificial intelligence techniques to aid in discovery of winning strategies for the Commons Game (CG). The game represents a common scenario in which multiple parties share the use of a self-replenishing resource. The resource deteriorates quickly if used indiscriminately. If used responsibly, however, the resource thrives. We consider the scenario one player uses hill climbing or particle swarm optimization to select the course of action, while the remaining N − 1 players use a fixed probability vector. We show that hill climbing and particle swarm optimization consistently generate winning strategies

Computational behavioral models in public goods games with migration between groups

In this study we have simulated numerically two models of linear public goods games where players are equally distributed among a given number of groups. Agents play in their group by using two simple sets of rules, called ‘blind’ and ‘rational’ model, respectively, that are inspired by the observed behavior of human participants in laboratory experiments. In addition, unsatisfied agents have the option of leaving their group and migrating to a new random one through probabilistic choices. Stochasticity, and the introduction of two types of players in the blind model, help simulate the heterogeneous behavior that is often observed in experimental work. Our numerical simulations of the corresponding dynamical systems show that being able to leave a group when unsatisfied favors contribution and avoids free-riding to a good extent in a range of the enhancement factor where defection would prevail without migration. Our numerical simulation presents results that are qualitatively in line with known experimental data when human agents are given the same kind of information about themselves and the other players in the group. This is usually not the case with customary mathematical models based on replicator dynamics or stochastic approaches. As a consequence, models like the ones described here may be useful for understanding experimental results and also for designing new experiments by first running cheap computational simulations instead of doing costly preliminary laboratory work. The downside is that models and their simulation tend to be less general than standard mathematical approaches.A A acknowledges the financial support of the Spanish Ministry of Science and Innovation under the Grant No. IJC2019-040967-I

Stability of Climate Coalitions in a Cartel Formation Game

We empirically test stability of climate change coalitions with the STAbility of Coalitions model (STACO). The model comprises twelve world regions and captures important dynamic aspects of the climate change problem. We apply the stability concept of internal and external stability to a cartel formation game. It is shown that only if benefits from global abatement are sufficiently high, stable coalitions emerge, though they only marginally improve upon the Nash equilibrium. We explain this phenomenon by analyzing the individual incentive structure of all regions and relate our results to the predictions of theory.International environmental agreements, Kyoto-Protocol, Cartel formation game, Non-cooperative game theory

Artificial Societies, Virtual Worlds, and Their Meaningful Integration

Artificial societies and virtual worlds are two areas of interest to modern social scientists that are distinctly separate in modern academic study, and are yet undeniably related. Artificial societies are multi-agent systems comprised of autonomous social agents, programmed with their own set of rules and behavior. While virtual worlds are occupied in large part by human controlled agents participating in a collective virtual experience and space. Within both types of virtual environments there can be found a scarcity of resources and intricate cross-entity interaction. This often results in the development and evolution of complex economic and cultural structures. In addition, by examining the modern research and common history shared by each field, it is possible to compile a set of shared attributes. This work attempts to capitalize on these shared features and promote a new type of integrated analysis that holds potential for future development in both fields. The concrete implementation of these ideas takes form as a simple economic model containing meaningful computer and human interaction as well as a framework designed for future extensibility

Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides

Background Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activity, but their versatility also requires the curated selection from a combinatorial sequence space. This space is too large for brute-force methods or currently known rational design approaches outside of machine learning. While there has been progress in using the design space to more effectively target AMP activity, a widely applicable approach has been elusive. The lack of transparency in machine learning has limited the advancement of scientific knowledge of how AMPs are related among each other, and the lack of general applicability for fully rational approaches has limited a broader understanding of the design space. Methods Here we combined an evolutionary method with rough set theory, a transparent machine learning approach, for designing antimicrobial peptides (AMPs). Our method achieves the customization of AMPs using supervised learning boundaries. Our system employs in vitro bacterial assays to measure fitness, codon-representation of peptides to gain flexibility of sequence selection in DNA-space with a genetic algorithm and machine learning to further accelerate the process. Results We use supervised machine learning and a genetic algorithm to find a peptide active against S. epidermidis, a common bacterial strain for implant infections, with an improved aggregation propensity average for an improved ease of synthesis. Conclusions Our results demonstrate that AMP design can be customized to maintain activity and simplify production. To our knowledge, this is the first time when codon-based genetic algorithms combined with rough set theory methods is used for computational search on peptide sequences

Spatio-temporal patterns act as computational mechanisms governing emergent behavior in robotic swarms

Our goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their self-coordinating emergent behavior, has proven ineffective, largely due to the swarm's inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micro-macro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm's emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

How insurance affects altruistic provision in threshold public good games

The occurrence and maintenance of cooperative behaviors in public goods systems have attracted great research attention across multiple disciplines. A threshold public goods game requires a minimum amount of contributions to be collected from a group of individuals for provision to occur. Here we extend the common binary-strategy combination of cooperation and defection by adding a third strategy, called insured cooperation, which corresponds to buying an insurance covering the potential loss resulted from the unsuccessful public goods game. Particularly, only the contributing agents can opt to be insured, which is an effort decreasing the amount of the potential loss occurring. Theoretical computations suggest that when agents face the potential aggregate risk in threshold public goods games, more contributions occur with increasing compensation from insurance. Moreover, permitting the adoption of insurance significantly enhances individual contributions and facilitates provision, especially when the required threshold is high. This work also relates the strategy competition outcomes to different allocation rules once the resulted contributions exceed the threshold point in populations nested within a dilemma