How mutation alters fitness of cooperation in networked evolutionary games

Cooperation is ubiquitous in every level of living organisms. It is known that spatial (network) structure is a viable mechanism for cooperation to evolve. Until recently, it has been difficult to predict whether cooperation can evolve at a network (population) level. To address this problem, Pinheiro et al. proposed a numerical metric, called Average Gradient of Selection (AGoS) in 2012. AGoS can characterize and forecast the evolutionary fate of cooperation at a population level. However, stochastic mutation of strategies was not considered in the analysis of AGoS. Here we analyzed the evolution of cooperation using AGoS where mutation may occur to strategies of individuals in networks. Our analyses revealed that mutation always has a negative effect on the evolution of cooperation regardless of the fraction of cooperators and network structures. Moreover, we found that mutation affects the fitness of cooperation differently on different social network structures.Comment: 6 pages, 5 figure

Aspiration Dynamics of Multi-player Games in Finite Populations

Studying strategy update rules in the framework of evolutionary game theory, one can differentiate between imitation processes and aspiration-driven dynamics. In the former case, individuals imitate the strategy of a more successful peer. In the latter case, individuals adjust their strategies based on a comparison of their payoffs from the evolutionary game to a value they aspire, called the level of aspiration. Unlike imitation processes of pairwise comparison, aspiration-driven updates do not require additional information about the strategic environment and can thus be interpreted as being more spontaneous. Recent work has mainly focused on understanding how aspiration dynamics alter the evolutionary outcome in structured populations. However, the baseline case for understanding strategy selection is the well-mixed population case, which is still lacking sufficient understanding. We explore how aspiration-driven strategy-update dynamics under imperfect rationality influence the average abundance of a strategy in multi-player evolutionary games with two strategies. We analytically derive a condition under which a strategy is more abundant than the other in the weak selection limiting case. This approach has a long standing history in evolutionary game and is mostly applied for its mathematical approachability. Hence, we also explore strong selection numerically, which shows that our weak selection condition is a robust predictor of the average abundance of a strategy. The condition turns out to differ from that of a wide class of imitation dynamics, as long as the game is not dyadic. Therefore a strategy favored under imitation dynamics can be disfavored under aspiration dynamics. This does not require any population structure thus highlights the intrinsic difference between imitation and aspiration dynamics

Inertia in spatial public goods games under weak selection

Due to limited cognitive skills for perceptual error or other emotional reasons, players may keep their current strategies even if there is a more promising choice. Such behavior inertia has already been studied, but its consequences remained unexplored in the weak selection limit. To fill this gap, we consider a spatial public goods game model where inertia is considered during the imitation process. By using the identity-by-descent method, we present analytical forms of the critical synergy factor $r^\star$, which determines when cooperation is favored. We find that inertia hinders cooperation, which can be explained by the decelerated coarsening process under weak selection. Interestingly, the critical synergy conditions for different updating protocols, including death-birth and birth-death rules, can be formally linked by the extreme limits of the inertia factor. To explore the robustness of our observations, calculations are made for different lattices and group sizes. Monte Carlo simulations also confirm the results

Evolutionary Game Theoretic Multi-Objective Optimization Algorithms and Their Applications

Multi-objective optimization problems require more than one objective functions to be optimized simultaneously. They are widely applied in many science fields, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conicting objectives. Most of the real world multi-objective optimization problems are NP-Hard problems. It may be too computationally costly to find an exact solution but sometimes a near optimal solution is sufficient. In these cases, Multi-Objective Evolutionary Algorithms (MOEAs) provide good approximate solutions to problems that cannot be solved easily using other techniques. However Evolutionary Algorithm is not stable due to its random nature, it may produce very different results every time it runs. This dissertation proposes an Evolutionary Game Theory (EGT) framework based algorithm (EGTMOA) that provides optimality and stability at the same time. EGTMOA combines the notion of stability from EGT and optimality from MOEA to form a novel and promising algorithm to solve multi-objective optimization problems. This dissertation studies three different multi-objective optimization applications, Cloud Virtual Machine Placement, Body Sensor Networks, and Multi-Hub Molecular Communication along with their proposed EGTMOA framework based algorithms. Experiment results show that EGTMOAs outperform many well known multi-objective evolutionary algorithms in stability, performance and runtime

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

The Role of Niche Signals in Self-organization in Society

This dissertation is concerned with the emergence of social patterns. The ability of groups of humans to bring order to both the physical and abstract realms may be our species’ most distinguishing characteristic. It is dependent upon our willingness to cooperate and otherwise coordinate, yet willingness alone is not sufficient for achieving coordinated outcomes on a large-scale because the informational demands of bottom-up organizing are high. Understanding the emergence of social order then requires, in part, understanding how information flows are structured in ways that allow groups to meet the informational demands of self-organization. Of particular importance in this regard are the patterns of person-to-person interactions. In contemporary social network research these interactions are often described as the conduits through which information flows, but person-to-person interactions are also the site and source of the coordination problem needing to be solved. To resolve this tension, network interactions must be patterned in ways that allow for the free flow of information, yet social networks most often exhibit high degrees of clustering, a characteristic which can impede the free flow of information and, thus, large-scale coordination. Does this mean bottom-up processes do not drive coordination within large groups? Is resolution by fiat the only way? Many have made the argument we create and tolerate authorities for precisely this reason, but is that the only viable mechanism for the establishment of large-scale coordination? Inspired by stigmergy, a form of communication used by social insects to coordinate hive activities, this dissertation explores the value of signals occurring outside or alongside of the person-to-person interactions studied using social network analysis. Social life features an abundance of small signals—often in the form of verbal or written communication, but also physical objects and even sounds and smells—potentially freighted with meanings or embedded knowledge. Several research traditions have regarded these signals as part of the fabric of social life, but is the information these signals yield patterned in a way that can help overcome the challenges of large-scale coordination? To begin to answer whether these signals can play a role in mass coordination, this dissertation takes three distinct approaches. The first analyses coupled differential equations describing a system in which a common resource environment is structured by the ongoing actor-to-actor interactions. This system is a modification of a canonical model of molecular self-organization, the hypercycle, and succeeds in organizing vastly more complex sets of interactions than the original. This confirms the information embedded in the environment can indeed be a powerful source of information for coordination. The second paper takes this formal insight into the lab to test whether the addition of a small number of extra-network signals can enable the emergence of conventions in a large, networked group of human participants. It can, and the probability of it happening depends on the strength of the extra-network signal and the topological features of the network. The final paper uses a unique dataset and topic modeling in an attempt to track the emergence of consensus around the themes in works of fiction. While there can be movement in the direction of consensus, the path lengths of the underlying network are too long to support large-scale consensus, a finding consistent with results of the experiment. Implications of these three findings are discussed in the conclusion.PHDSociologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138683/1/atwell_1.pd