Network Modularity is essential for evolution of cooperation under uncertainty

Strategic behavior of large language models and the role of game structure versus contextual framing

This paper investigates the strategic behavior of large language models (LLMs) across various game-theoretic settings, scrutinizing the interplay between game structure and contextual framing in decision-making. We focus our analysis on three advanced LLMs-GPT-3.5, GPT-4, and LLaMa-2-and how they navigate both the intrinsic aspects of different games and the nuances of their surrounding contexts. Our results highlight discernible patterns in each model's strategic approach. GPT-3.5 shows significant sensitivity to context but lags in its capacity for abstract strategic decision making. Conversely, both GPT-4 and LLaMa-2 demonstrate a more balanced sensitivity to game structures and contexts, albeit with crucial differences. Specifically, GPT-4 prioritizes the internal mechanics of the game over its contextual backdrop but does so with only a coarse differentiation among game types. In contrast, LLaMa-2 reflects a more granular understanding of individual game structures, while also giving due weight to contextual elements. This suggests that LLaMa-2 is better equipped to navigate the subtleties of different strategic scenarios while also incorporating context into its decision-making, whereas GPT-4 adopts a more generalized, structure-centric strategy.

The functional aspects of selective exposure for collective decision-making under social influence

Opinion diversity is crucial for collective decision-making, but maintaining it becomes challenging in the face of social influence. We propose selective exposure as an endogenous mechanism that preserves opinion diversity by forming exclusive subgroups of like-minded individuals, or echo chambers, which have been often perceived as an obstacle to achieving collective intelligence. We consider situations where a group of agents collectively make decisions about the true state of nature with the assumption that agents update their opinions by adopting the aggregated opinions of their information sources (i.e., naïve learning), or alternatively, replace incongruent sources with more like-minded others without adjusting their opinions (i.e., selective exposure). Individual opinions at steady states reached under these dynamics are then aggregated to form collective decisions, and their quality is assessed. The results suggest that the diversity-reducing effects of social influence are effectively confined within subgroups formed by selective exposure. More importantly, strong propensities for selective exposure maintain the quality of collective decisions at a level as high as that achieved in the absence of social influence. In contrast, naïve learning allows groups to reach consensuses, which are more accurate than initial individual opinions, but significantly undermines the quality of collective decisions.

Airbnb and neighborhood crime: The incursion of tourists or the erosion of local social dynamics?

The proliferation of internet-based home-sharing platforms like Airbnb has raised heated debates, with many in the general public believing that the presence of Airbnb listings can lead to an increase in crime and disorder in residential neighborhoods. Despite the importance of this debate to residents, policymakers, and other stakeholders, few studies have examined the causal linkage between Airbnb listings and crime in neighborhoods. We conduct the first such empirical test in Boston neighborhoods, focusing on two potential mechanisms: (1) the inflow of tourists might generate or attract crime; and (2) the creation of transient properties undermines local social dynamics. Corresponding to these mechanisms, we examine whether the number of tourists (approximated with reviews) or the prevalence of listings predict more incidents of private conflict, social disorder, and violence both concurrently and in the following year. We find evidence that increases in Airbnb listings–but not reviews–led to more violence in neighborhoods in later years. This result supports the notion that the prevalence of Airbnb listings erodes the natural ability of a neighborhood to prevent crime, but does not support the interpretation that elevated numbers of tourists bring crime with them.

Multiplex communities and the emergence of international conflict

Advances in community detection reveal new insights into multiplex and multilayer networks. Less work, however, investigates the relationship between these communities and outcomes in social systems. We leverage these advances to shed light on the relationship between the cooperative mesostructure of the international system and the onset of interstate conflict. We detect communities based upon weaker signals of affinity expressed in United Nations votes and speeches, as well as stronger signals observed across multiple layers of bilateral cooperation. Communities of diplomatic affinity display an expected negative relationship with conflict onset. Ties in communities based upon observed cooperation, however, display no effect under a standard model specification and a positive relationship with conflict under an alternative specification. These results align with some extant hypotheses but also point to a paucity in our understanding of the relationship between community structure and behavioral outcomes in networks.

Population Structure Promotes the Evolution of Intuitive Cooperation and Inhibits Deliberation

Spatial structure is one of the most studied mechanisms in evolutionary game theory. Here, we explore the consequences of spatial structure for a question which has received considerable empirical and theoretical attention in recent years, but has not yet been studied from a network perspective: whether cooperation relies on intuitive predispositions or deliberative self-control. We examine this question using a model which integrates the “dual-process” framework from cognitive science with evolutionary game theory, and considers the evolution of agents who are embedded within a social network and only interact with their neighbors. In line with past work in well-mixed populations, we find that selection favors either the intuitive defector strategy which never deliberates, or the dual-process cooperator strategy which intuitively cooperates but uses deliberation to switch to defection when doing so is payoff-maximizing. We find that sparser networks (i.e., smaller average degree) facilitate the success of dual-process cooperators over intuitive defectors, while also reducing the level of deliberation that dual-process cooperators engage in; and that these results generalize across different kinds of networks. These observations demonstrate the important role that spatial structure can have not just on the evolution of cooperation, but on the co-evolution of cooperation and cognition.

Disease implications of animal social network structure: A synthesis across social systems

The disease costs of sociality have largely been understood through the link between group size and transmission. However, infectious disease spread is driven primarily by the social organization of interactions in a group and not its size. We used statistical models to review the social network organization of 47 species, including mammals, birds, reptiles, fish and insects by categorizing each species into one of three social systems, relatively solitary, gregarious and socially hierarchical. Additionally, using computational experiments of infection spread, we determined the disease costs of each social system. We find that relatively solitary species have large variation in number of social partners, that socially hierarchical species are the least clustered in their interactions, and that social networks of gregarious species tend to be the most fragmented. However, these structural differences are primarily driven by weak connections, which suggest that different social systems have evolved unique strategies to organize weak ties. Our synthetic disease experiments reveal that social network organization can mitigate the disease costs of group living for socially hierarchical species when the pathogen is highly transmissible. In contrast, highly transmissible pathogens cause frequent and prolonged epidemic outbreaks in gregarious species. We evaluate the implications of network organization across social systems despite methodological challenges, and our findings offer new perspective on the debate about the disease costs of group living. Additionally, our study demonstrates the potential of meta-analytic methods in social network analysis to test ecological and evolutionary hypotheses on cooperation, group living, communication and resilience to extrinsic pressures.

Identifying Causes of Patterns in Ecological Networks: Opportunities and Limitations

Ecological networks depict the interactions between species, mainly based on observations in the field. The information contained in such interaction matrices depends on the sampling design, and typically, compounds preferences (specialization) and abundances (activity). Null models are the primary vehicles to disentangle the effects of specialization from those of sampling and abundance, but they ignore the feedback of network structure on abundances. Hence, network structure, as exemplified here by modularity, is difficult to link to specific causes. Indeed, various processes lead to modularity and to specific interaction patterns more generally. Inferring (co)evolutionary dynamics is even more challenging, as competition and trait matching yield identical patterns of interactions. A satisfactory resolution of the underlying factors determining network structure will require substantial additional information, not only on independently assessed abundances, but also on traits, and ideally on fitness consequences as measured in experimental setups.

Fair Topologies: Community Structures and Network Hubs Drive Emergence of Fairness Norms

Fairness has long been argued to govern human behavior in a wide range of social, economic, and organizational activities. The sense of fairness, although universal, varies across different societies. In this study, using a computational model, we test the hypothesis that the topology of social interaction can causally explain some of the cross-societal variations in fairness norms. We show that two network parameters, namely, community structure, as measured by the modularity index, and network hubiness, represented by the skewness of degree distribution, have the most significant impact on emergence of collective fair behavior. These two parameters can explain much of the variations in fairness norms across societies and can also be linked to hypotheses suggested by earlier empirical studies in social and organizational sciences. We devised a multi-layered model that combines local agent interactions with social learning, thus enables both strategic behavior as well as diffusion of successful strategies. By applying multivariate statistics on the results, we obtain the relation between network structural features and the collective fair behavior.

Study on material base and action mechanism of compound Danshen dripping pills for treatment of atherosclerosis based on modularity analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine (TCM) has been widely used in China and its surrounding countries in clinical treatments for centuries-long time. However, due to the complexity of TCM constituents, both action mechanism and material base of TCM remain nearly unknown.

AIM OF THE STUDY

The present study was designed to uncover the action mechanism and material base of TCM in a low-cost manner.

MATERIALS AND METHODS

Compound Danshen dripping pills (DSP) is a widely used TCM for treatment of atherosclerosis, and was researched here to demonstrate the effectiveness of our method. We constructed a heterogeneous network for DSP, identified the significant network module, and analyzed the primary pharmacological units by performing GO and pathways enrichment analysis.

RESULTS

Two significant network modules were identified from the heterogeneous network of DSP, and three compounds out of four hub nodes in the network were found to intervene in the process of atherosclerosis. Moreover, 13 out of 20 enriched pathways that were ranked in top 10 corresponding to both the two pharmacological units were found to be involved in the process of atherosclerosis.

CONCLUSIONS

Quercetin, luteolin and apigenin may be the main active compounds which modulate the signaling pathways, such as metabolism of xenobiotics by cytochrome P450, retinol metabolism, etc. The present method helps reveal the action mechanism and material base of DSP for treatment of atherosclerosis.

Sparse cliques trump scale-free networks in coordination and competition

Cooperative behavior, a natural, pervasive and yet puzzling phenomenon, can be significantly enhanced by networks. Many studies have shown how global network characteristics affect cooperation; however, it is difficult to understand how this occurs based on global factors alone, low-level network building blocks, or motifs are necessary. In this work, we systematically alter the structure of scale-free and clique networks and show, through a stochastic evolutionary game theory model, that cooperation on cliques increases linearly with community motif count. We further show that, for reactive stochastic strategies, network modularity improves cooperation in the anti-coordination Snowdrift game and the Prisoner's Dilemma game but not in the Stag Hunt coordination game. We also confirm the negative effect of the scale-free graph on cooperation when effective payoffs are used. On the flip side, clique graphs are highly cooperative across social environments. Adding cycles to the acyclic scale-free graph increases cooperation when multiple games are considered; however, cycles have the opposite effect on how forgiving agents are when playing the Prisoner's Dilemma game.

Dynamics of Complex Systems Built as Coupled Physical, Communication and Decision Layers

This paper proposes a simple model to capture the complexity of multilayer systems where their constituent layers affect, and are affected by, each other. The physical layer is a circuit composed by a power source and resistors in parallel. Every individual agent aims at maximizing its own delivered power by adding, removing or keeping the resistors it has; the delivered power is in turn a non-linear function that depends on the other agents’ behavior, its own internal state, its global state perception, the information received from its neighbors via the communication network and a randomized selfishness. We develop an agent-based simulation to analyze the effects of number of agents (system size), communication network topology, communication errors and the minimum power gain that triggers a behavioral change on the system dynamic. Our results show that a wave-like behavior at macro-level (caused by individual changes in the decision layer) can only emerge for a specific system size. The ratio between cooperators and defectors depends on the minimum gain assumed—lower minimal gains lead to less cooperation, and vice-versa. Different communication network topologies imply different levels of power utilization and fairness at the physical layer, and a certain level of error in the communication layer induces more cooperation.

Solvothermally synthesized graphene nanosheets supporting spinel NiFe2O4 nanoparticles as an efficient electrocatalyst for the oxygen reduction reaction

Solvothermally synthesized graphene nanosheets supporting spinel NiFe₂O₄ nanoparticles was prepared under hydrothermal treatment, exhibiting both excellent activity for oxygen reduction and superior long-term durability to commercial Pt/C.