A fundamental question in biology is URLanisms integrate sensory and social evidence to make decisions . However, few models describe how both these streams of information can be combined to optimize choices . Here we develop a normative model for collective decision making in a network of agents performing a two-alternative forced choice task. We assume that rational (Bayesian) agents in this network make private measurements , and observe the decisions of their neighbors until they accumulate sufficient evidence to make an irreversible choice . As each agent communicates its decision to those observing it, the flow of social information is described by a directed graph. The decision-making process in this setting is intuitive, but can be complex. We describe when and how the absence of a decision of a neighboring agent communicates social information, and how an agent must marginalize over all unobserved decisions. We also show how decision thresholds and network connectivity affect group evidence accumulation, and describe the dynamics of decision making in social cliques. Our model provides a bridge between the abstractions used in the economics literature and the evidence accumulator models used widely in neuroscience and psychology.
To make decisions we are guided by the evidence we collect, as well as the opinions of friends and neighbors. How do we integrate our private beliefs with information we obtain from our social network? To understand the strategies humans use to do so it is useful to compare them to observers that optimally integrate all evidence . Here we derive network models of rational (Bayes optimal) agents who accumulate private measurements and observe decisions of their neighbors to make an irreversible choice between two options. The resulting information exchange dynamics has interesting properties: When one option is preferred, the absence of a decision can be increasingly informative over time. In recurrent networks an absence of a decision can lead to a sequence of belief updates akin to those in the literature on common knowledge. Information obtained from observing repeated non-decisions is independent of realization, unless the private information of agents is redundant. On the other hand, in larger networks a single decision can trigger a cascade of agreements and disagreements that depend on the private information agents have gathered. Our approach provides a bridge between social decision making models in the economics literature , which largely ignore the temporal dynamics of decisions, and the single-observer evidence accumulator models used widely in neuroscience and psychology.
R
A fundamental question in biology is URLanisms integrate sensory and social evidence to make decisions . However, few models describe how both these streams of information can be combined to optimize choices
To make decisions we are guided by the evidence we collect, as well as the opinions of friends and neighbors. How do we integrate our private beliefs with information we obtain from our social network? To understand the strategies humans use to do so it is useful to compare them to observers that optimally integrate all evidence
R
develop a normative model for collective decision making in a network of agents performing a two-alternative forced choice task. We assume that rational (Bayesian) agents in this network make private measurements , and observe the
derive network models of rational (Bayes optimal) agents who accumulate private measurements and observe
D
until they accumulate sufficient evidence
R
. As each agent communicates its decision to those observing it, the flow of social information is described by a directed graph. The decision-making process in this setting is intuitive, but can be complex. We describe when and how the
between two options. The resulting information exchange dynamics has interesting properties: When one option is preferred, the absence of a decision can be increasingly informative over time. In recurrent networks an
R
of a neighboring agent communicates social information, and how an agent must marginalize over all unobserved decisions. We also show how decision thresholds and network connectivity affect group evidence accumulation, and describe the dynamics of decision making in social cliques. Our model
can lead to a sequence of belief updates akin to those in the literature on common knowledge. Information obtained from observing repeated non-decisions is independent of realization, unless the private information of agents is redundant. On the other hand, in larger networks a single decision can trigger a cascade of agreements and disagreements that depend on the private information agents have gathered. Our approach
R
the abstractions used
social decision making models
R
and the
, which largely ignore the temporal dynamics of decisions, and the single-observer