Abundant resources can trigger reduced consumption: Unveiling the paradox of excessive scrounging.

In ecological contexts, it is conventionally expected that increased food availability would boost consumption, particularly when animals prioritize maximizing their food intake. This paper challenges this conventional wisdom by conducting an in-depth game-theoretic analysis of a basic foraging model, in which animals must choose between intensive food searching as producers or moderate searching while relying on group members as scroungers. Our study reveals that, under certain circumstances, increasing food availability can amplify the inclination to scrounge to such an extent that it leads to a reduction in animals' food consumption compared to scenarios with limited food availability. We further illustrate a similar phenomenon in a model capturing free-riding dynamics among workers in a company. We demonstrate that, under certain reward mechanisms, enhancing workers' production capacities can inadvertently trigger a surge in free-riding behavior, leading to both diminished group productivity and reduced individual payoffs. Our findings provide intriguing insights into the complex relationships between individual and group performances, as well as the intricate mechanisms underlying the emergence of free-riding behavior in competitive environments.

Public communication can facilitate low-risk coordination under surveillance.

Consider a sub-population of rebels aiming at initiating a revolution. To avoid initializing a failed revolution, rebels would first strive to estimate their "power", which is often correlated with their number. However, especially in non-democratic countries, rebels avoid disclosing themselves. This poses a significant challenge for rebels: estimating their number while minimizing the risk of being identified as rebels. This paper introduces a distributed computing framework to study this question. Our main insight is that the communication pattern plays a crucial role in achieving such a task. Specifically, we distinguish between public communication, in which each message announced by an individual can be viewed by all its neighbors, and private communication, in which each message is received by one neighbor. We describe a simple protocol in the public communication model that allows rebels to estimate their number while keeping a negligible risk of being identified as rebels. The proposed protocol, inspired by historical events, can be executed covertly even under extreme conditions of surveillance. Conversely, we show that under private communication, protocols of similar simplicity are either inefficient or non-covert. These results suggest that public communication can facilitate the emergence of revolutions in non-democratic countries.

On the role of hypocrisy in escaping the tragedy of the commons.

We study the emergence of cooperation in large spatial public goods games. Without employing severe social-pressure against "defectors", or alternatively, significantly rewarding "cooperators", theoretical models typically predict a system collapse in a way that is reminiscent of the "tragedy-of-the-commons" metaphor. Drawing on a dynamic network model, this paper demonstrates how cooperation can emerge when the social-pressure is mild. This is achieved with the aid of an additional behavior called "hypocrisy", which appears to be cooperative from the external observer's perspective but in fact hardly contributes to the social-welfare. Our model assumes that social-pressure is induced over both defectors and hypocritical players, though the extent of which may differ. Our main result indicates that the emergence of cooperation highly depends on the extent of social-pressure applied against hypocritical players. Setting it to be at some intermediate range below the one employed against defectors allows a system composed almost exclusively of defectors to transform into a fully cooperative one quickly. Conversely, when the social-pressure against hypocritical players is either too low or too high, the system remains locked in a degenerate configuration.

Intermittent inverse-square Lévy walks are optimal for finding targets of all sizes.

Lévy walks are random walk processes whose step lengths follow a long-tailed power-law distribution. Because of their abundance as movement patterns of biological organisms, substantial theoretical efforts have been devoted to identifying the foraging circumstances that would make such patterns advantageous. However, despite extensive research, there is currently no mathematical proof indicating that Lévy walks are, in any manner, preferable strategies in higher dimensions than one. Here, we prove that in finite two-dimensional terrains, the inverse-square Lévy walk strategy is extremely efficient at finding sparse targets of arbitrary size and shape. Moreover, this holds even under the weak model of intermittent detection. Conversely, any other intermittent Lévy walk fails to efficiently find either large targets or small ones. Our results shed new light on the Lévy foraging hypothesis and are thus expected to affect future experiments on animals performing Lévy walks.

Reinforcement Learning Enables Resource Partitioning in Foraging Bats.

Every evening, from late spring to mid-summer, tens of thousands of hungry lactating female lesser long-nosed bats (Leptonycteris yerbabuenae) emerge from their roost and navigate over the Sonoran Desert, seeking for nectar and pollen [1, 2]. The bats roost in a huge maternal colony that is far from the foraging grounds but allows their pups to thermoregulate [3] while the mothers are foraging. Thus, the mothers have to fly tens of kilometers to the foraging sites-fields with thousands of Saguaro cacti [4, 5]. Once at the field, they must compete with many other bats over the same flowering cacti. Several solutions have been suggested for this classical foraging task of exploiting a resource composed of many renewable food sources whose locations are fixed. Some animals randomly visit the food sources [6], and some actively defend a restricted foraging territory [7-11] or use simple forms of learning, such as "win-stay lose-switch" strategy [12]. Many species have been suggested to follow a trapline, that is, to revisit the food sources in a repeating ordered manner [13-22]. We thus hypothesized that lesser long-nosed bats would visit cacti in a sequenced manner. Using miniature GPS devices, aerial imaging, and video recordings, we tracked the full movement of the bats and all of their visits to their natural food sources. Based on real data and evolutionary simulations, we argue that the bats use a reinforcement learning strategy that requires minimal memory to create small, non-overlapping cacti-cores and exploit nectar efficiently, without social communication.

Ant collective cognition allows for efficient navigation through disordered environments.

The cognitive abilities of biological organisms only make sense in the context of their environment. Here, we study longhorn crazy ant collective navigation skills within the context of a semi-natural, randomized environment. Mapping this biological setting into the 'Ant-in-a-Labyrinth' framework which studies physical transport through disordered media allows us to formulate precise links between the statistics of environmental challenges and the ants' collective navigation abilities. We show that, in this environment, the ants use their numbers to collectively extend their sensing range. Although this extension is moderate, it nevertheless allows for extremely fast traversal times that overshadow known physical solutions to the 'Ant-in-a-Labyrinth' problem. To explain this large payoff, we use percolation theory and prove that whenever the labyrinth is solvable, a logarithmically small sensing range suffices for extreme speedup. Overall, our work demonstrates the potential advantages of group living and collective cognition in increasing a species' habitable range.

Limits on reliable information flows through stochastic populations.

Biological systems can share and collectively process information to yield emergent effects, despite inherent noise in communication. While man-made systems often employ intricate structural solutions to overcome noise, the structure of many biological systems is more amorphous. It is not well understood how communication noise may affect the computational repertoire of such groups. To approach this question we consider the basic collective task of rumor spreading, in which information from few knowledgeable sources must reliably flow into the rest of the population. We study the effect of communication noise on the ability of groups that lack stable structures to efficiently solve this task. We present an impossibility result which strongly restricts reliable rumor spreading in such groups. Namely, we prove that, in the presence of even moderate levels of noise that affect all facets of the communication, no scheme can significantly outperform the trivial one in which agents have to wait until directly interacting with the sources-a process which requires linear time in the population size. Our results imply that in order to achieve efficient rumor spread a system must exhibit either some degree of structural stability or, alternatively, some facet of the communication which is immune to noise. We then corroborate this claim by providing new analyses of experimental data regarding recruitment in Cataglyphis niger desert ants. Finally, in light of our theoretical results, we discuss strategies to overcome noise in other biological systems.

Individual versus collective cognition in social insects.

The concerted responses of eusocial insects to environmental stimuli are often referred to as collective cognition at the level of the colony. To achieve collective cognition, a group can draw on two different sources: individual cognition and the connectivity between individuals. Computation in neural networks, for example, is attributed more to sophisticated communication schemes than to the complexity of individual neurons. The case of social insects, however, can be expected to differ. This is because individual insects are cognitively capable units that are often able to process information that is directly relevant at the level of the colony. Furthermore, involved communication patterns seem difficult to implement in a group of insects as they lack a clear network structure. This review discusses links between the cognition of an individual insect and that of the colony. We provide examples for collective cognition whose sources span the full spectrum between amplification of individual insect cognition and emergent group-level processes.

A locally-blazed ant trail achieves efficient collective navigation despite limited information.

Any organism faces sensory and cognitive limitations which may result in maladaptive decisions. Such limitations are prominent in the context of groups where the relevant information at the individual level may not coincide with collective requirements. Here, we study the navigational decisions exhibited by Paratrechina longicornis ants as they cooperatively transport a large food item. These decisions hinge on the perception of individuals which often restricts them from providing the group with reliable directional information. We find that, to achieve efficient navigation despite partial and even misleading information, these ants employ a locally-blazed trail. This trail significantly deviates from the classical notion of an ant trail: First, instead of systematically marking the full path, ants mark short segments originating at the load. Second, the carrying team constantly loses the guiding trail. We experimentally and theoretically show that the locally-blazed trail optimally and robustly exploits useful knowledge while avoiding the pitfalls of misleading information.

Confidence sharing: an economic strategy for efficient information flows in animal groups.

Social animals may share information to obtain a more complete and accurate picture of their surroundings. However, physical constraints on communication limit the flow of information between interacting individuals in a way that can cause an accumulation of errors and deteriorated collective behaviors. Here, we theoretically study a general model of information sharing within animal groups. We take an algorithmic perspective to identify efficient communication schemes that are, nevertheless, economic in terms of communication, memory and individual internal computation. We present a simple and natural algorithm in which each agent compresses all information it has gathered into a single parameter that represents its confidence in its behavior. Confidence is communicated between agents by means of active signaling. We motivate this model by novel and existing empirical evidences for confidence sharing in animal groups. We rigorously show that this algorithm competes extremely well with the best possible algorithm that operates without any computational constraints. We also show that this algorithm is minimal, in the sense that further reduction in communication may significantly reduce performances. Our proofs rely on the Cramér-Rao bound and on our definition of a Fisher Channel Capacity. We use these concepts to quantify information flows within the group which are then used to obtain lower bounds on collective performance. The abstract nature of our model makes it rigorously solvable and its conclusions highly general. Indeed, our results suggest confidence sharing as a central notion in the context of animal communication.