Real-world Social Systems Research Articles

Evolutionary game selection creates cooperative environments.

The emergence of collective cooperation in competitive environments is a well-known phenomenon in biology, economics, and social systems. While most evolutionary game models focus on the evolution of strategies for a fixed game, how strategic decisions coevolve with the environment has so far mostly been overlooked. Here, we consider a game selection model where not only the strategies but also the game can change over time following evolutionary principles. Our results show that coevolutionary dynamics of games and strategies can induce novel collective phenomena, fostering the emergence of cooperative environments. When the model is taken on structured populations the architecture of the interaction network can significantly amplify pro-social behavior, with a critical role played by network heterogeneity and the presence of clustered groups of similar players, distinctive features observed in real-world populations. By unveiling the link between the evolution of strategies and games for different structured populations, our model sheds new light on the origin of social dilemmas ubiquitously observed in real-world social systems.

Incorporating reputation into reinforcement learning can promote cooperation on hypergraphs

Most previous studies that investigate the evolutionary dynamics of cooperation in the multi-agent social dilemma only consider the pairwise interactions. However, pairwise interactions alone cannot adequately characterize the higher-order interaction relationships that are commonly present in real-world social systems. In this study, we propose a spatial public goods game on uniform random hypergraphs with reputation mechanism and self-regarding Q-learning algorithm, where agents take an action based on the Q-value and update their reputation by the first-order evaluation norm. We define the reward as a function of the agent’s payoff and their reputation influenced by the social attention degree. Simulation results indicate that an increase in the social attention degree can effectively facilitate cooperation, as social reputation incentivizes agents to adopt cooperative actions to obtain optimal rewards. Additionally, we find that the discount factor and the learning rate both significantly impact the evolution of cooperation, with the influence of the learning rate on the evolution of cooperation being dependent on the discount factor. Finally, the robustness of this model is demonstrated based on the results of theoretical analysis.

Hyperlink communities in higher-order networks

Abstract Many networks can be characterized by the presence of communities, which are groups of units that are closely linked. Identifying these communities can be crucial for understanding the system’s overall function. Recently, hypergraphs have emerged as a fundamental tool for modelling systems where interactions are not limited to pairs but may involve an arbitrary number of nodes. In this study, we adopt a dual approach to community detection and extend the concept of link communities to hypergraphs. This extension allows us to extract informative clusters of highly related hyperedges. We analyse the dendrograms obtained by applying hierarchical clustering to distance matrices among hyperedges across a variety of real-world data, showing that hyperlink communities naturally highlight the hierarchical and multiscale structure of higher-order networks. Moreover, hyperlink communities enable us to extract overlapping memberships from nodes, overcoming limitations of traditional hard clustering methods. Finally, we introduce higher-order network cartography as a practical tool for categorizing nodes into different structural roles based on their interaction patterns and community participation. This approach aids in identifying different types of individuals in a variety of real-world social systems. Our work contributes to a better understanding of the structural organization of real-world higher-order systems.

Polarization and multiscale structural balance in signed networks

Polarization, or a division into mutually hostile groups, is a common feature of social systems. It is studied in Structural Balance Theory in terms of semicycles in signed networks. However, enumerating semicycles is computationally expensive, so approximations are often needed. Here we introduce the Multiscale Semiwalk Balance approach for measuring the degree of balance (DoB) in (un)directed, (un)weighted signed networks by approximating semicycles with closed semiwalks. It allows selecting the resolution of analysis appropriate for assessing DoB motivated by the Locality Principle, which posits that patterns in shorter cycles are more important than in longer ones. Our approach overcomes several limitations affecting walk-based approximations and provides methods for assessing DoB at various scales, from graphs to individual nodes, and for clustering signed networks. We demonstrate its effectiveness by applying it to real-world social systems, which leads to explainable results for networks with expected patterns (polarization in the US Congress) and a more nuanced perspective for other systems. Our work may facilitate studying polarization and structural balance in a variety of contexts and at multiple scales.

Animal social networks: an introduction for complex systems scientists

AbstractMany animals live in societies where individuals frequently interact socially with each other. The social structures of these systems can be studied in depth by means of network analysis. A large number of studies on animal social networks in many species have in recent years been carried out in the biological research field of animal behaviour and have provided new insights into behaviour, ecology and social evolution. This line of research is currently not so well connected to the field of complex systems as could be expected. The purpose of this article is to provide an introduction to animal social networks for complex systems scientists and highlight areas of synergy. We believe that an increased integration of animal social networks with the interdisciplinary field of complex systems, and networks would be beneficial for various reasons. Increased collaboration between researchers in this field and biologists studying animal social systems could be valuable in solving challenges that are of importance to animal social network research. Furthermore, animal social networks provide the opportunity to investigate hypotheses about complex systems across a range of natural real-world social systems. In this article, we describe what animal social networks are and main research themes where they are studied; we give an overview of the methods commonly used to study animal social networks; we highlight challenges in the study of animal social networks where complex systems expertise may be particularly valuable; and we consider aspects of animal social networks that may be of particular interest to complex systems researchers. We hope that this will help to facilitate further interdisciplinary collaborations involving animal social networks and further integration of these networks into the field of complex systems.

Homophily Based on Few Attributes Can Impede Structural Balance.

Homophily between agents and structural balance in connected triads of agents are complementary mechanisms thought to shape social groups leading to, for instance, consensus or polarization. To capture both processes in a unified manner, we propose a model of pair and triadic interactions. We consider N fully connected agents, where each agent has G underlying attributes, and the similarity between agents in attribute space (i.e., homophily) is used to determine the link weight between them. For structural balance we use a triad-updating rule where only one attribute of one agent is changed intentionally in each update, but this also leads to accidental changes in link weights and even link polarities. The link weight dynamics in the limit of large G is described by a Fokker-Planck equation from which the conditions for a phase transition to a fully balanced state with all links positive can be obtained. This "paradise state" of global cooperation is, however, difficult to achieve requiring G>O(N^{2}) and p>0.5, where the parameter p captures a willingness for consensus. Allowing edge weights to be a consequence of attributes naturally captures homophily and reveals that many real-world social systems would have a subcritical number of attributes necessary to achieve structural balance.

Inspiration-wise swarm intelligence meta-heuristics for continuous optimisation: a survey - part I

In many science fields, such as engineering, administration, transportation, economics and biology, among many others, optimisation problem is a handy required tool. Global optimisation techniques aim to find the best solution in a set of feasible solutions to a problem. Currently, there are numerous optimisation techniques. In general, the problems to be optimised are complex, nonlinear and in some cases may be intractable. Meta-heuristics are general algorithmic frameworks adaptable to various optimisation problems and are generally applied to highly complex problems. Swarm intelligence represents intelligent models inspired by real-world social systems, based on interaction and organisation between simple agents to perform simple tasks. Nowadays, there are many swarm-based meta-heuristics. In this survey, we take advantage of the inspiration behind the strategies to contribute an inspiration-oriented novel taxonomy for the current state-of-the-art of swarm-oriented optimisation methods. The overall survey, which will be divided into three separate parts, provides a review of swarm-based meta-heuristic, which is commonly employed to solve complex continuous optimisations, aiming at building a inspiration-based taxonomy for such search strategies. In this part of the survey, we review meta-heuristics that are guided by some interesting human relation's characteristics and those that are inspired by physical system's properties.

Social Simulation Models at the Ethical Crossroads.

Computational models of group opinion dynamics are one of the most active fields of sociophysics. In recent years, advances in model complexity and, in particular, the possibility to connect these models with detailed data describing individual behaviors, preferences and activities, have opened the way for the simulations to describe quantitatively selected, real world social systems. The simulations could be then used to study 'what-if' scenarios for opinion change campaigns, political, ideological or commercial. The possibility of the practical application of the attitude change models necessitates that the research community working in the field should consider more seriously the moral aspects of their efforts, in particular the potential for their use for unintended goals. The paper discusses these issues, and offers a suggestion for a new research direction: using the attitude models to increase the awareness and detection of social manipulation cases. Such research would offer a scientific challenge and meet the ethical criteria.

Artificial intelligence in peer review: How can evolutionary computation support journal editors?

With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors’ workloads, are treated as trade secrets by publishing houses and are not shared publicly. To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies. The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy). Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems.

Asymptotic theory of time varying networks with burstiness and heterogeneous activation patterns

The recent availability of large-scale, time-resolved and high quality digital datasets has allowed for a deeper understanding of the structure and properties of many real-world networks. The empirical evidence of a temporal dimension prompted the switch of paradigm from a static representation of networks to a time varying one. In this work we briefly review the framework of time-varying-networks in real world social systems, especially focusing on the activity-driven paradigm. We develop a framework that allows for the encoding of three generative mechanisms that seem to play a central role in the social networks’ evolution: the individual’s propensity to engage in social interactions, its strategy in allocate these interactions among its alters and the burstiness of interactions amongst social actors. The functional forms and probability distributions encoding these mechanisms are typically data driven. A natural question arises if different classes of strategies and burstiness distributions, with different local scale behavior and analogous asymptotics can lead to the same long time and large scale structure of the evolving networks. We consider the problem in its full generality, by investigating and solving the system dynamics in the asymptotic limit, for general classes of ties allocation mechanisms and waiting time probability distributions. We show that the asymptotic network evolution is driven by a few characteristics of these functional forms, that can be extracted from direct measurements on large datasets.

Social Contagion of Risk Perceptions in Environmental Management Networks

An important requisite for improving risk communication practice related to contentious environmental issues is having a better theoretical understanding of how risk perceptions function in real-world social systems. Our study applied Scherer and Cho's social network contagion theory of risk perception (SNCTRP) to cormorant management (a contentious environmental management issue) in the Great Lakes Basin to: (1) assess contagion effects on cormorant-related risk perceptions and individual factors believed to influence those perceptions and (2) explore the extent of social contagion in a full network (consisting of interactions between and among experts and laypeople) and three "isolated" models separating different types of interactions from the full network (i.e., expert-to-expert, layperson-to-layperson, and expert-to-layperson). We conducted interviews and administered questionnaires with experts (e.g., natural resource professionals) and laypeople (e.g., recreational and commercial anglers, business owners, bird enthusiasts) engaged in cormorant management in northern Lake Huron (n = 115). Our findings generally support the SNCTRP; however, the scope and scale of social contagion varied considerably based on the variables (e.g., individual risk perception factors), actors (i.e., experts or laypeople), and interactions of interest. Contagion effects were identified more frequently, and were stronger, in the models containing interactions between experts and laypeople than in those models containing only interactions among experts or laypeople.

Linguistic characterization of preference relations as a basis for choice in social systems

In assessing the applicability of the theories of choice to social systems, one cannot escape the fact that such systems are generally much too complex and much too ill-defined to be susceptible of analysis in precise, quantita? tive terms. For example, as discussed in [1], [2], there is considerable uncertainty in the valuation of interpersonal as well as individual utilities among a collection of individuals. Another complicating factor is that the preference relations are frequently conditioned on variables whose values are unknown or, at least, not well-defined; in addition, they may be, and frequently are, interdependent in the sense that the preference relation of an individual may be affected by his or her perception of the preference relations of other members of the collection. Furthermore, the underlying decision processes are, in most cases, multi-stage processes with poorly defined horizons, uncertain dynamics and vague constraints. These are but a few of the many considerations which suggest that, in their present form, the mathematical theories of choice may well be excessively precise in relation to the overwhelming complexity of real-world social systems. A less precise alternative to the conventional methods of quantitative analysis is provided by the so-called linguistic approach [3], [4], in which words rather than numbers are employed to characterize approximately the values of variables as well as the relations between them. As a matter of fact, we frequently resort to such characterizations in everyday dis? course, e.g., when we describe the age of an individual by a label such as young, not young, very young, not very young, etc., rather than by a number in the set U = {0, 1, 2,..., 100}. In this case, the labels in question