Delving into behavioral responses when humans face social dilemmas
The modeling of social systems has recently attracted a renewed attention as a result of the Data Science revolution. Ideally, we would like to develop tools and methods that allow in-silico simulations of real-world societal scenarios and systems. To this end, it is imperative to inform models with as much details as possible about human behavior at various scales. This constitutes nowadays a challenge due to our current limited knowledge of the laws describing most human behavioral responses. In this talk, we describe recent advances in this direction by discussing the results of several experiments involving humans -in some cases a few, in others hundreds- playing a diversity of social dilemmas. We also identify the experimental (data) and theoretical challenges in the study of techno-social systems, and propose a way to tackle such problems.
Climate Change and Global Governance in an Uncertain World
Francisco C. Santos
When attempting to avoid global warming, individuals often face a social dilemma in which, besides securing future benefits, it is also necessary to reduce the chances of future losses. Unfortunately, individuals, regions or nations may opt to be “free riders”, hoping to benefit from the efforts of others while choosing not to make any effort themselves. Moreover, nations and their leaders seek a collective goal that is shadowed by the uncertainty of its achievement. Such types of uncertainties have repeatedly happened throughout human history from group hunting to voluntary adoption of public health measures and other prospective choices. In this talk, I will discuss a population dynamics approach to a broad class of cooperation problems in which attempting to minimize future losses turns the risk of failure into a central issue in individual decisions. Our results suggest that global coordination for a common good should be attempted by segmenting tasks in many small to medium sized groups in which perception of risk is high. Moreover, whenever the perception of risk is low — as it is presently the case — we find that a polycentric approach involving multiple institutions is more effective than that associated with a single, global one, indicating that a bottom-up approach, setup at a local scale, provides a better ground on which to attempt a solution for such a complex and global dilemma. Finally, I will discuss the impact on public goods dilemmas of uncertainty in collective goals, heterogeneous political networks, obstinate players and wealth inequality, including a distribution of wealth representative of existing inequalities among nations.
The role of social influence in competitive strategical games
Roberta Amato, Kaj-Kolja Kleineberg, Jan Haerter, Albert Diaz-Guilera
Mixed dynamics of strategic and social imitative behaviors have shown to alter the outcome of competitive games significantly. However, in reality these interactions take place in different social and strategic contexts or domains, which can have important implications for the dynamic of the system.
To study the effect of different contexts, we consider a multiplex system of two different layers: a game network, where the dynamics obey to a competitive game rules and a social network, where individuals exchange opinions on their strategies through a biased voter model dynamic. We consider the four main games (the Harmony Game, the Stag Hunt Game, the Prisoners Dilemma and the Hawk-Dove Game) with the Fermi imitation rule to give the possibility of making mistakes. We assume that individuals have an intrinsic tendency to be consistent in both layers but they can also lie by playing with one strategy and consenting with their friends on the other. Importantly, the topologies of the layers composing the system are not expected to be identical neither completely unrelated, meaning that individuals that engage in strategic interactions can have a social relationship but can also be unrelated in the social context. To account for these features, we use a model which can generate multiplex networks with realistic layer topologies and where both the popularity of individuals as well as their similarity are correlated to varying degrees between the different contexts.
We show that the interplay between different contexts can lead to new behaviors. In particular, we show that the emergence of localized communities of highly synchronized individuals in the Stag Hunt Game in isolated network can be hindered if the coupling to the opinion context is present. In addition, we discuss the impact of popularity and similarity correlations between the different contexts on the dynamic of the system.
Coexistence of multiple public goods in a bacterial colony
Carlos Gracia-Lázaro, Yamir Moreno, Joaquín Sanz, Mauro Moreno
Some aspects of the interactions between microorganisms (such as the siderophore production or the resilience to antibiotics) can be studied from the perspective of cooperation. Some microbes (producers or cooperators) pay a metabolic cost while non-producers or cheaters benefit from the public goods without paying that cost. In some theoretical models, ecological populations of microorganisms are operationally defined as groups of coexisting individuals that are highly clustered on the genotypic and phenotypic levels. In these models, each bacterium is characterized by the genes it possesses and has assigned a fitness related with the public good.
In this work we propose a theoretical model to study the effect of the Horizontal Gene Transfer mechanism HGT in a bacterial colony. As a novelty, we consider a variable number G of genes altogether, each one associated to a public good (e.g., iron scavenging molecules, an enzyme for digesting sucrose, resistance to antibiotics, proteins to extracellular protein digestion, etc). We assume that the G genes are intermediate frequency genes, which are susceptible to be acquired by HGT or also by asexual reproduction. Each bacterium has assigned a fitness related with the totality of the public goods.
We have found that, when more than a public good is considered, the appearance and spreading of a survival trait does not imply a shift from the previous strategic equilibrium: a small ratio of HGT is enough to not occur clonal sweeps. For intermediate values of the HGT rate, this mechanism allows the spreading of necessary genes in the colonization phases, while for very low rates of HGT, a decrease in density is observed in the colonization phases, and only the producers’ genotypes survive. In addition, we have found that there is a range of the HGT ratio, compatible with the experimental values, that maximizes the biodiversity.
Topology-dependent rationality and quantal response equilibria in structured populations
Sabin Roman, Seth Bullock, Markus Brede
The assumption of perfect rationality, an underlying concept of the notion of Nash equilibria, which are frequently used to reason about the decision making of actors in game theory, has been shown to be violated in many examples of decision-making in the real world.
In contrast, bounded rationality, is a more versatile concept to model decisions of human actors . Here we explore a graded notion of rationality in socio-ecological systems of networked actors. Employing a framework developed in , we describe actors' rationality parameters via their place in a social network and quantify system rationality via the average symmetrised Kullback-Leibler divergence between the games Nash and Quantal Response equilibria.
In this context, previous work  has argued that scale-free topologies maximise a system's overall rationality. Here, we show that while it is true that increasing degree-heterogeneity of complex networks enhances rationality, rationality-optimal configurations are not scale-free. We provide analytical arguments complemented by numerical optimisation experiments to demonstrate that core-periphery networks composed of a few dominant hub nodes surrounded by a periphery of very low degree nodes give strikingly smaller differences to rationality than scale-free networks. We also propose a class of topologies than can interpolate between random graphs and core-periphery networks, which allows us to gain insights into the structure of the search landscape a system has to navigate on its way towards enhanced rationality, which provides insight why truly optimal configurations might be exceedingly rare.
These results shed further light on the role of social networks for the decision making of networks of interacting players and provide insight on the interplay between the topological structure of socio-ecological systems and their collective cognitive behaviour.
 Kasthurirathna, D., and Piraveenan, M. (2015). Emergence of scale-free characteristics in socio-ecological systems with bounded rationality. Scientific reports, 5:10448.
The evolution of conditional moral assessment in indirect reciprocity
Tatsuya Sasaki, Isamu Okada, Yutaka Nakai
Indirect reciprocity is a major mechanism in the maintenance of cooperation among unfamiliar individuals. Indirect reciprocity leads to conditional cooperation according to social norms that discriminate the good (those who deserve to be rewarded with help) and the bad (those who should be punished by refusal of help). Despite intensive research, however, there is no definitive consensus on what social norms best promote cooperation through indirect reciprocity, and it remains unclear even how those who refuse help to the bad should be assessed. Here we propose a new simple norm called ‘Staying’ that prescribes to abstain from assessment, with the focal individual’s image remaining unchanged, if its opponent has a bad image. We fully analyse the Staying norm in terms of evolutionary game theory and unveil that Staying is most effective in establishing cooperation, compared to the prevailing social norms which rely on constant monitoring and unconditional assessment. The excellence of Staying suggests the limitation of strict application of moral judgment.
Solitary observation in indirect reciprocity
Isamu Okada, Tatsuya Sasaki and Yutaka Nakai
The intensive studies on indirect reciprocity seldom break down an assumption of public information. All the players correspondently mistake about a tag of player if an error in perception occurs, and thus, a tag of each player is unique and never differ among the others. Considering the assessment cost bears the second-order free-rider problem. We theoretically analyze the solitary observation and compare with the shared monitoring (public information). To model a giving game, we assume that well-mixed infinite players in a population participate in the game using a continuous-entry model (Brandt and Sigmund, 2005). Players in the model consists of three norm-adopters: unconditional cooperators who always give a help, all-out defectors who always refuse a help, and conditional cooperators who cooperate to those assessed as good. In a game with solitary observation, a donor, a recipient, and an observer are randomly selected. The image of the donor in the eyes of the observer only is updated while the eyes of the rest of discriminators in the population do not change the image of the donor. To explore evolutionary dynamics of the private scores, we consider an analysis of a marginal value of a good reputation (Ohtsuki et al., 2015). In the framework, any expected probability of that a player's image in the eyes of a discriminator is saturated if the games infinitely continue, and then the expected payoffs of each player are calculated. The solitary observation and the shared monitoring go to different results. Our analysis shows that the tolerant indirect reciprocity with justified defection increases the Pareto efficiency in combination with the unconditional cooperators in the solitary observation than in the public information. Our result sheds light on the role of unconditional cooperators while so far they were unvalued as a second-order free-riders.
Exploring Dynamic Environments Using Stochastic Search Strategies
Carlos Adolfo Piña García, J. Mario Siqueiros, Gustavo Carreon, Carlos Gershenson
In this paper, we conduct a literature review of laws of motion based on stochastic search strategies which are mainly focused on exploring highly dynamic environments. In this regard, stochastic search strategies represent an interesting alternative to cope with uncertainty and reduced perceptual capabilities. This study aims to present an introductory overview of research in terms of directional rules and searching methods mainly based on bio-inspired approaches.
This study critically examines the role of animal searching behavior applied to random walk models using stochastic rules and kinesis or taxis. The aim of this study is to examine existing techniques and to select relevant work on random walks and analyze their actual contributions. In this regard, we cover a wide range of displacement events with an orientation mechanism given by a reactive behavior or a source-seeking behavior. Finally, we conclude with a discussion concerning the usefulness of using optimal foraging strategies as a reliable methodology.
Stochastic search strategies plays an important role in terms of facing environmental uncertainty. Therefore, the present paper pretends to uncover the most insightful directional rules inspired by stochastic methods, statistical physics and random walks. Likewise, we consider these strategies as an emergent phenomenon (formation of global patterns from solely local interactions) which is a frequent and fascinating theme in the scientific literature both popular and academic.
The aim of this paper is to examine existing techniques and do a comprehensive analysis to understand state-of-the-art, trends and research gaps. It is important to mention that these strategies can be used in the field of robotics as exploration and discovery algorithms with the aim to speeding up searching tasks.
Stochastic search strategies are mainly inspired by optimal foraging theory which involves animal search behavior as an alternative for facing highly dynamic environments. Thus, these strategies can be viewed as a correlated process which may consists of displacements only broken by successive reorientation events. Strategies such as: Lévy walk, ballistic motion and correlated random walk are well known examples of foraging strategies, which are subject to statistical properties derived from Lévy stochastic processes.
Recent evidence suggests that search strategies are mainly related to availability, quality and quantity of publicly accessible data on animal movement and Artificial Intelligence techniques. Recently, synthetic experiments have shown that what really matters is where the explorer diffuses, not the manner by which the explorer gets there. We therefore, decided to concentrate on what we considered to be some of the more significant developments in stochastic search models. It is important to mention that for organization purposes, we have split in two categories as follows: stochastic rules and directional rules (taxes).