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.

Simulating the dynamics of dispersal and dispersal ability in fragmented populations with mate-finding Allee effects.

We consider the spatial propagation and genetic evolution of model populations comprising multiple subpopulations, each distinguished by its own characteristic dispersal rate. Mate finding is modeled in accord with the assumption that reproduction is based on random encounters between pairs of individuals, so that the frequency of interbreeding between two subpopulations is proportional to the product of local population densities of each. The resulting nonlinear growth term produces an Allee effect, whereby reproduction rates are lower in sparsely populated areas; the distribution of dispersal rates that evolves is then highly dependent upon the population's initial spatial distribution. In a series of numerical test cases, we consider how these dynamics affect lattice-like arrangements of population fragments, and investigate how a population's initial fragmentation determines the dispersal rates that evolve as a habitat is colonized. First, we consider a case where initial population fragments coincide with habitat islands, within which death rates differ from those that apply outside; the presence of inhospitable exterior regions exaggerates Allee effect-driven reductions in dispersal ability. We then examine how greater distances separating adjacent population fragments lead to more severe reductions in dispersal ability. For populations of a fixed initial magnitude, fragmentation into smaller, denser patches leads not only to greater losses of dispersal ability, but also helps ensure the population's long-term persistence, emphasizing the trade-offs between the benefits and risks of rapid dispersal under Allee effects. Next, simulations of well-established populations disrupted by localized depopulation events illustrate how mate-finding Allee effects and spatial heterogeneity can drive a population's dispersal ability to evolve either downward or upward depending on conditions, highlighting a qualitative distinction between population fragmentation and habitat heterogeneity. A final test case compares populations that are fragmented across multiple scales, demonstrating how differences in the relative scales of micro- and macro-level fragmentation can lead to qualitatively different evolutionary outcomes.

Are automated accounts driving scholarly communication on Twitter? a case study of dissemination of COVID-19 publications.

From a network perspective, this study analyzes 659 users mentioning sampled COVID-19 articles 10 or more times on Twitter with a focus on their roles in facilitating the process of scholarly communication. Different from existing studies, we consider both the user types and the automation of accounts to profile influential users in the network of research dissemination. Our study found that similar to academic users, non-academic users can also be active players in communicating scientific publications. The results highlight the intensive interactions between human users and automated accounts, including bots and cyborgs, which accounted for 45% of connections among the top users. This study also demonstrates the important role of automated accounts in initiating and facilitating research dissemination. Specifically, (1) bot-assisted academic publishers showed the highest amplifier scores, which measures a user's tendency of being the first to share information and reach out to others within their trusted networks, (2) 5.28% of the selected articles was first tweeted by automated research feeds, ranking the fourth among the 22 classified user groups, and (3) bot-assisted publishers and automated feeds of generic topics and news alerts were highly ranked in authority, a network measure to quantify the degree to which a user consumes important resources of relevant topics. In the conclusion section, we discuss future directions to improve the validity of Twitter metrics in assessing research impacts.

Adaptation strategies and collective dynamics of extraction in networked commons of bistable resources.

When populations share common-pool resources (CPRs), individuals decide how much effort to invest towards resource extraction and how to allocate this effort among available resources. We investigate these dual aspects of individual choice in networked games where resources undergo regime shifts between discrete quality states (viable or depleted) depending on collective extraction levels. We study the patterns of extraction that emerge on various network types when agents are free to vary extraction from each CPR separately to maximize their short-term payoffs. Using these results as a basis for comparison, we then investigate how results are altered if agents fix one aspect of adaptation (magnitude or allocation) while letting the other vary. We consider two constrained adaptation strategies: uniform adaptation, whereby agents adjust their extraction levels from all CPRs by the same amount, and reallocation, whereby agents selectively shift effort from lower- to higher-quality resources. A preference for uniform adaptation increases collective wealth on degree-heterogeneous agent-resource networks. Further, low-degree agents retain preferences for these constrained strategies under reinforcement learning. Empirical studies have indicated that some CPR appropriators ignore-while others emphasize-allocation aspects of adaptation; our results demonstrate that structural patterns of resource access can determine which behavior is more advantageous.

Myopic reallocation of extraction improves collective outcomes in networked common-pool resource games.

When individuals extract benefits from multiple resources, the decision they face is twofold: besides choosing how much total effort to exert for extraction, they must also decide how to allocate this effort. We focus on the allocation aspect of this choice in an iterated game played on bipartite networks of agents and common-pool resources (CPRs) that degrade linearly in quality as extraction increases. When CPR users attempt to reallocate their extraction efforts among resources to maximize their own payoffs in the very next round (that is, myopically), collective wealth is increased. Using a heterogeneous mean-field approach, we estimate how these reallocations affect the payoffs of CPR users of different degrees within networks having different levels of degree heterogeneity. Focusing specifically on Nash equilibrium initial conditions, which represent the patterns of over-exploitation that result from rational extraction, we find that networks with greater heterogeneity among CPR degrees show greater improvements over equilibrium due to reallocation. When the marginal utility of extraction diminishes, these reallocations also reduce wealth inequality. These findings emphasize that CPR users' adaptive reallocations of effort-a behavior that previously-studied network evolutionary game models typically disallow by construction-can serve to direct individuals' self-interest toward the collective good.

An exploration of motivations for two screen viewing, social interaction behaviors, and factors that influence viewing intentions.

This study explores whether, and how, motivations for two screen viewing predicted social interaction behaviors and subsequent viewing intention of TV programs. A total of 453 respondents who responded that they use social networking sites (SNSs) via smartphones and actively watch entertainment programs completed an online survey questionnaire. In agreement with uses and gratifications assumptions, motivations for TSV predicted distinctive sets of social interaction behaviors, which mediated the influence of motivations on viewing intentions. Respondents' two screen viewing was meaningfully related with social interaction, engagement with programs, information seeking, and passing time. Results suggest that two screen viewing could provide shared experiences nourishing social capital and reintegrate TV audiences by social adhesive resulting from TV with SNSs.