Optimal use of simplified social information in sequential decision-making

Abstract

Social animals can improve their decisions by attending to those made by others. The benefit of this social information must be balanced against the costs of obtaining and processing it. Previous work has focused on rational agents that respond optimally to a sequence of prior decisions. However, full decision sequences are potentially costly to perceive and process. As such, animals may rely on simpler social information, which will affect the social behaviour they exhibit. Here, I derive the optimal policy for agents responding to simplified forms of social information. I show how the behaviour of agents attending to the aggregate number of previous choices differs from those attending to just the most recent prior decision, and I propose a hybrid strategy that provides a highly accurate approximation to the optimal policy with the full sequence. Finally, I analyse the evolutionary stability of each strategy, showing that the hybrid strategy dominates when cognitive costs are low but non-zero, while attending to the most recent decision is dominant when costs are high. These results show that agents can employ highly effective social decision-making rules without requiring unrealistic cognitive capacities, and indicate likely ecological variation in the social information different animals attend to.

Avoiding costly mistakes in groups: The evolution of error management in collective decision making

“…Other possible strategies include the use of the relative difference in the numbers of individuals favoring each option [ 58 , 59 ], the incorporation of decision order (e.g. by up-weighting recent choices [ 60 , 61 ], the consideration of conflicting preferences [ 62 ], or the incorporation of social information by a one-time update in the evidence [ 34 , 35 ]. The exact strategy of social information integration can influence the temporal dynamic of the collective process.…”

Modeling the effects of environmental and perceptual uncertainty using deterministic reinforcement learning dynamics with partial observability

“…The proposed dynamics are therefore a suitable tool to advance theoretical research in cognitive ecology, which studies how animals acquire, retain, and use information within their ecology, evolution and behavior [75,76]. Within this area, research has begun to ask how agents' may evolve non-veridical or incomplete representations of the world [62,77,78]; the dynamic model presented here offers a tool to study the effect of non-veridical representations in greater depth.…”

Collective decision-making under changing social environments among agents adapted to sparse connectivity

“…I consider a group of n agents sequentially choosing between two options, A and B. The true utilities of A and B are unknown by the agents and may differ between agents; for agent i these are denoted as U A,i and U B , i , and I define the difference between these utilities as x i ≡ U A,i − U B,i (Mann, 2018, 2020). Agents are assumed to be rational decision-makers who seek to maximise the expected utility of their decision Von Neumann and Morgenstern (2007) based on the total information I that they have.…”

“…Living in groups provides many advantages to social animals (Krause and Ruxton, 2002), including the opportunity to make use of ‘social information’ to improve the quality of decision-making (Ward et al, 2011; Wolf et al, 2013). Choices made by others are often a useful indicator of which option is best for oneself; if one trusts that others are acting rationally (Mann, 2018), and that they have similar preferences to oneself (Mann, 2020), then their decisions provide valuable clues about the quality of different options. However, when others are also attending to each other this can cause information cascades (Anderson and Holt, 1997; Bikhchandani et al, 1992), rendering all but a few decisions meaningless as sources of information.…”

“…In animal biology, a greater focus has been placed on identifying the 'interaction rules' that specify the probability that a focal individual will make a specific decision conditioned on the choices made by other animals (typically con-specifics) within the group (Arganda et al, 2012;Mann, 2018Mann, , 2020Mann, , 2021Pérez-Escudero and De Polavieja, 2011). Of particular interest is the goal of deriving theoretically motivated interaction rules that closely match empirical observations from collective decision-making in animal groups, whether in the laboratory Arganda et al (2012); Kadak and Miller (2020); Miller et al (2013);Pérez-Escudero and De Polavieja (2011) or the field Farine et al (2014).…”