Chemotaxis emerges as the optimal solution to cooperative search games
Open accessAbstract
Cooperative search games are collective tasks where all agents share the same goal of reaching a target in the shortest time while limiting energy expenditure and avoiding collisions. Here we show that the equations that characterize the optimal strategy are identical to a long-known phenomenological model of chemotaxis, the directed motion of microorganisms guided by chemical cues. Within this analogy, the substance to which searchers respond acts as the memory over which agents share information about the environment. The actions of writing, erasing, and forgetting are equivalent to production, consumption, and degradation of chemoattractant. The rates at which these biochemical processes take place are tightly related to the parameters that characterize the decision-making problem, such as learning rate, costs for time, control, collisions and their tradeoffs, as well as the attitude of agents toward risk. We establish a dictionary that maps notions from decision-making theory to biophysical observables in chemotaxis, and vice versa. Our results offer a fundamental explanation of why search algorithms that mimic microbial chemotaxis can be very effective and suggest how to optimize their performance.
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Discontinuous phase transitions in information-processing strategies have also been reported within the information bottleneck framework.
“Discontinuous phase transitions in informationprocessing strategies have also been reported within the information bottleneck framework [38-42].”
From Theoretical Analysis of Resource-Induced Phase Transitions in Estimation Strategies (2026) · cites this paper as [42]
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- Authors
- Pezzotta, A., Adorisio, M., & Celani, A.
- Affiliations
- Scuola Internazionale Superiore di Studi Avanzati (IT)
- The Abdus Salam International Centre for Theoretical Physics (ICTP) (IT)
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2018-10-01
- Volume
- 98
- Issue
- 4
- ISSN
- 2470-0045 (ISSN-L)
- 2470-0053
- OpenAlex
- W2783610569
- Cited by
- 17
- References
- 32
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 5 · 2022: 2 · 2021: 2 · 2019: 1
- FWCI
- 0.27
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical review. E (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Diffusion and Search Dynamics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- chemotaxis, cooperative search, group decision making, swarm intelligence, information sharing, collision avoidance, energy expenditure, risk preferences, learning rate
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