# Individual bias and fluctuations in collective decision making: from algorithms to Hamiltonians

## TL;DR

This paper maps a collective decision-making spin model to a Hamiltonian framework, revealing how social interactions and fluctuations influence consensus formation and metastability in finite systems.

## Contribution

It provides a statistical physics interpretation of the model, deriving exact solutions and analyzing the effects of fluctuations, system size, and initial conditions.

## Key findings

- Exact solutions for the model's thermodynamics on complete graphs.
- Temperature as a measure of fluctuations impacts decision dynamics.
- Finite-size effects influence convergence to metastable states.

## Abstract

In this paper, we reconsider the spin model suggested recently to understand some features of collective decision making among higher organisms [A.T. Hartnett et al., Phys. Rev. Lett. 116 (2016) 038701]. Within the model, the state of an agent $i$ is described by the pair of variables corresponding to its opinion $S_i=\pm 1$ and a bias $\omega_i$ towards any of the opposing values of $S_i$. Collective decision making is interpreted as an approach to the equilibrium state within the non-linear voter model subject to a social pressure and a probabilistic algorithm. Here, we push such physical analogy further and give the statistical physics interpretation of the model, describing it in terms of the Hamiltonian of interaction and looking for the equilibrium state via explicit calculation of its partition function. We show that depending on the assumptions about the nature of social interactions two different Hamiltonians can be formulated, which can be solved with different methods. In such an interpretation the temperature serves as a measure of fluctuations, not considered before in the original model. We find exact solutions for the thermodynamics of the model on the complete graph. The general analytical predictions are confirmed using individual-based simulations. The simulations allow us also to study the impact of system size and initial conditions in the collective decision making in finite-sized systems, in particular with respect to convergence to metastable states.