Diversity, Fairness, and Sustainability in Population Protocols

TL;DR

This paper introduces a population protocol that maintains diversity and fairness among agents, ensuring balanced task allocation based on importance weights, with minimal memory and no need for agents to know others' states.

Contribution

It presents a simple, distributed protocol for diverse, fair, and sustainable task allocation in population protocols, accommodating arbitrary initial distributions and importance weights.

Findings

Protocol converges in O(w^2 n log n) rounds.

Agents do not need to know other agents' states or weights.

Ensures long-term fairness and prevents any task from vanishing.

Abstract

Over the years, population protocols with the goal of reaching consensus have been studied in great depth. However, many systems in the real-world do not result in all agents eventually reaching consensus, but rather in the opposite: they converge to a state of rich diversity. Consider for example task allocation in ants. If eventually all ants perform the same task, then the colony will perish (lack of food, no brood care, etc.). Then, it is vital for the survival of the colony to have a diverse set of tasks and enough ants working on each task. What complicates matters is that ants need to switch tasks periodically to adjust the needs of the colony; e.g., when too many foragers fell victim to other ant colonies. Moreover, all tasks are equally important and maybe they need to keep certain proportions in the distribution of the task. How can ants keep a healthy and balanced allocation of tasks? To answer this question, we propose a simple population protocol for $n$ agents on a complete graph and an arbitrary initial distribution of $k$ colours (tasks). We assume that each colour $i$ has an associated weight (importance) $w_i \geq 1$. By denoting $w$ as the sum of the weights of different colours, we show that the protocol converges in $O(w^2 n \log n)$ rounds to a configuration where the number of agents supporting each colour $i$ is concentrated on the fair share $w_in/w$ and will stay concentrated for a large number of rounds, w.h.p. Our protocol has many interesting properties: agents do not need to know other colours and weights in the system, and our protocol requires very little memory per agent. Furthermore, the protocol guarantees fairness meaning that over a long period each agent has each colour roughly a number of times proportional to the weight of the colour. Finally, our protocol also fulfils sustainability meaning that no colour ever vanishes.