A cognitive computational approach to social and collective decision-making

Abstract

Collective dynamics play a key role in everyday decision-making. Whether social influence promotes the spread of accurate information and ultimately results in adaptive behavior or leads to false information cascades and maladaptive social contagion strongly depends on the cognitive mechanisms underlying social interactions. Here we argue that cognitive modeling, in tandem with experiments that allow collective dynamics to emerge, can mechanistically link cognitive processes at the individual and collective levels. We illustrate the strength of this cognitive computational approach with two highly successful cognitive models that have been applied to interactive group experiments: evidence accumulation and reinforcement learning models. We show how these approaches make it possible to simultaneously study (i) how individual cognition drives social systems, (ii) how social systems drive individual cognition, and (iii) the dynamic feedback processes between the two layers.

Visual-spatial dynamics drive adaptive social learning in immersive environments

“…For instance, whether adaptation is driven by asocial or social cues, and whether these mechanisms operate independently or interactively with one another. Modeling dynamic strategy selection in social contexts is particularly difficult, because the availability and quality of social information constantly changes as result of both individual decisions and group dynamics 30 . Thus, this gap represents both theoretical and empirical challenges, requiring new methods to capture the complex and dynamic nature of human adaptability, which we seek to address in this current study.…”

Risk-sensitive learning is a winning strategy for leading an urban invasion

“…Heeding previous calls [53,54,55], our study provides an analytical solution to facilitate productive research on proximate and ultimate explanations of seemingly flexible (or not) behaviour: because we publicly provide step-by-step code to map a full pathway from behaviour to mechanisms through to selection and adaptation (see Data and materials availability), which can be tailored to specific research questions. The reinforcement learning model, for example, generalises to, in theory, a variety of choice-option paradigms [56], and these learning models can be extended to estimate asocial and social influence on individual decision making (e.g., [57,24,58,59,60]), facilitating useful insight into the multi-faceted feedback process between individual cognition and social systems [61]. Our open-access analytical resource thus allows researchers to move beyond the umbrella term behavioural flexibility, and to biologically inform and interpret their science-only then can we begin to meaningfully compare behavioural variation across taxa and/or contexts.…”

The Psychology of Collectives

“…A necessary requirement for understanding the psychology of collectives is to go beyond modeling and simulation toward calibration and validation of models with empirical data. Along this line, Tump et al (2024) illustrate various examples of experimental results and hypotheses drawn from agent-based models of collective decision-making. Research in collective behavior needs proper empirical benchmarks to compare models, introduce networked components of social interaction, and identify causal mechanisms and environmental conditions that lead to different collective outcomes.…”

“…Collectives evolve through interactions of individual cognitions, network structures, and problem environments (De Dreu et al, 2024; Hahn, 2024; Wheatley et al, 2024). These interactions occur on different time scales (Tump et al, 2024) and affect the robustness and adaptivity of collectives (Schweitzer et al, 2024).…”

“…Other authors argue for integrating different models by identifying a general principle for systems with properties shared among the different models (Smaldino et al, 2024), drawing parallels between models of different collective phenomena such as crowd and opinion dynamics (Warren et al, 2024), or combining models of collective dynamics occurring at different scales. For example, Tump et al (2024) argue for a two-layered computational-modeling framework that combines evidence-accumulation and reinforcement-learning processes to capture the dynamic interplay of individual cognition and social systems.…”