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1. A Model and Analysis of House-Hunting in Ant Colonies

   Zhang, E. ; Zhao, J. ; Lynch, N. ( January 2021 , 8th Workshop on Biological Distributed Algorithms (BDA))

   We study the problem of house-hunting in ant colonies, where ants reach consensus on a new nest and relocate their colony to that nest, from a distributed computing perspective. We propose a house-hunting algorithm that is biologically inspired by Temnothorax ants. Each ant is modelled as a probabilistic agent with limited power, and there is no central control governing the ants. We show a (log n) lower bound on the running time of our proposed house-hunting algorithm, where n is the number of ants. Further, we show a matching upper bound of expected O(log n) rounds for environments with only one candidate nest for the ants to move to. Our work provides insights into the house-hunting process, giving a perspective on how environmental factors such as nest qualities or a quorum rule can affect the emigration process. In particular, we find that a quorum threshold that is high enough causes transports to the inferior nest to cease to happen after O(log n) rounds when there are two nests in the environment. 

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2. An Upper and Lower Bound for the Convergence Time of House-Hunting in Temnothorax Ant Colonies.

   Zhang, E. ; Zhao, J. ; Lynch, N. ( January 2022 , Journal of computational biology)

   We study the problem of house-hunting in ant colonies, where ants reach consensus on a new nest and relocate their colony to that nest, from a distributed computing perspective. We propose a house-hunting algorithm that is biologically inspired by Temnothorax ants. Each ant is modeled as a probabilistic agent with limited power, and there is no central control governing the ants. We show an O( log n) lower bound on the running time of our proposed house-hunting algorithm, where n is the number of ants. Furthermore, we show a matching upper bound of expected O( log n) rounds for environments with only one candidate nest for the ants to move to. Our work provides insights into the house-hunting process, giving a perspective on how environmental factors such as nest quality or a quorum rule can affect the emigration process. 

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3. Lack of Quorum Sensing Leads to Failure of Consensus in Temnothorax Ant Emigration

   Zhao, J. ; Su, L. ; Lynch, N. ( January 2021 , 23rd International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS 2021))

   We investigate the importance of quorum sensing in the success of house-hunting of emigrating Temnothorax ant colonies. Specifically, we show that the absence of the quorum sensing mechanism leads to failure of consensus during emigrations. We tackle this problem through the lens of distributed computing by viewing it as a natural distributed consensus algorithm. We develop an agent-based model of the house-hunting process, and use mathematical tools such as conditional probability, concentration bounds and Markov mixing time to rigorously prove the negative impact of not employing the quorum sensing mechanism on emigration outcomes. Our main result is a high probability bound for failure of consensus without quorum sensing in a two-new-nest environment, which we further extend to the general multiple-new-nest environments. We also show preliminary evidence that appropriate quorum sizes indeed help with consensus during emigrations. Our work provides theoretical foundations to analyze why Temnothorax ants evolved to utilize the quorum rule in their house-hunting process. 

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4. Algorithms for Noisy Broadcast with Erasures

   https://doi.org/10.4230/LIPIcs.ICALP.2018.153  

   Grossman, Ofer ; Haeupler, Bernhard ; Mohanty, Sidhanth ( July 2018 , International Colloquium on Automata, Languages and Programming)

   he noisy broadcast model was first studied by [Gallager, 1988] where an n-character input is distributed among n processors, so that each processor receives one input bit. Computation proceeds in rounds, where in each round each processor broadcasts a single character, and each reception is corrupted independently at random with some probability p. [Gallager, 1988] gave an algorithm for all processors to learn the input in O(log log n) rounds with high probability. Later, a matching lower bound of Omega(log log n) was given by [Goyal et al., 2008]. We study a relaxed version of this model where each reception is erased and replaced with a `?' independently with probability p, so the processors have knowledge of whether a bit has been corrupted. In this relaxed model, we break past the lower bound of [Goyal et al., 2008] and obtain an O(log^* n)-round algorithm for all processors to learn the input with high probability. We also show an O(1)-round algorithm for the same problem when the alphabet size is Omega(poly(n)). 

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5. The Power of Population Effect in Temnothorax Ants House-Hunting: A Computational Modeling Approach.

   Zhao, J. ; Lynch, N. ; Pratt, S.C. ( January 2022 , Journal of computational biology)

   The decentralized cognition of animal groups is both a challenging biological problem and a potential basis for bio-inspired design. In this study, we investigated the house-hunting algorithm used by emigrating colonies of Temnothorax ants to reach consensus on a new nest. We developed a tractable model that encodes accurate individual behavior rules, and estimated our parameter values by matching simulated behaviors with observed ones on both the individual and group levels. We then used our model to explore a potential, but yet untested, component of the ants’ decision algorithm. Specifically, we examined the hypothesis that incorporating site population (the number of adult ants at each potential nest site) into individual perceptions of nest quality can improve emigration performance. Our results showed that attending to site population accelerates emigration and reduces the incidence of split decisions. This result suggests the value of testing empirically whether nest site scouts use site population in this way, in addition to the well demonstrated quorum rule. We also used our model to make other predictions with varying degrees of empirical support, including the high cognitive capacity of colonies and their rational time investment during decision-making. Additionally, we provide a versatile and easy-to-use Python simulator that can be used to explore other hypotheses or make testable predictions. It is our hope that the insights and the modeling tools can inspire further research from both the biology and computer science community. 

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