Social learning in a multi-agent system

In a persistent multi-agent system, it should be possible for new agents to benefit from the accumulated learning of more experienced agents. Parallel reasoning can be applied to the case of newborn animals, and thus the biological literature on social learning may aid in the construction of effective multi-agent systems. Biologists have looked at both the functions of social learning and the mechanisms that enable it. Many researchers have focused on the cognitively complex mechanism of imitation; we will also consider a range of simpler mechanisms that could more easily be implemented in robotic or software agents. Research in artificial life shows that complex global phenomena can arise from simple local rules. Similarly, complex information sharing at the system level may result from quite simple individual learning rules. We demonstrate in simulation that simple mechanisms can outperform imitation in a multi-agent system, and that the effectiveness of any social learning strategy will depend on the agents' environment. Our simple mechanisms have obvious advantages in terms of robustness and design costs

Social learning mechanisms compared in a simple environment

Social learning can be adaptive, but little is known about the underlying mechanisms. Many researchers have focused on imitation but this may have led to simpler mechanisms being underestimated. We demonstrate in simulation that imitative learning is not always the best strategy for a group-living animal, and that the effectiveness of any such strategy will depend on details of the environment and the animal's lifestyle. We show that observations of behavioural convergence or "traditions" might suggest effective social learning, but are meaningless considered alone

Social Learning in a Multi-Agent System

In a persistent multi-agent system, it should be possible for new agents to benefit from the accumulated learning of more experienced agents. Parallel reasoning can be applied to the case of newborn animals, and thus the biological literature on social learning may aid in the construction of effective multi-agent systems. Biologists have looked at both the functions of social learning and the mechanisms that enable it. Many researchers have focused on the cognitively complex mechanism of imitation; we will also consider a range of simpler mechanisms that could more easily be implemented in robotic or software agents. Research in artificial life shows that complex global phenomena can arise from simple local rules. Similarly, complex information sharing at the system level may result from quite simple individual learning rules. We demonstrate in simulation that simple mechanisms can outperform imitation in a multi-agent system, and that the effectiveness of any social learning strategy will depend on the agents' environment. Our simple mechanisms have obvious advantages in terms of robustness and design costs

Simulating bounded rationality: Optimality modelling without an optimality commitment

If you accept that evolved agents will be only boundedly rational, what are the consequences for the tools we use to make models of and construct theories about behaviour? In particular, consider methods like agent-based modelling -- with its roots in game theory and incorporating a notion of optimization or maximizing across alternatives -- do such methods remain viable? Or can cognitive science only deal with contingent, historical accounts of behaviour? I will argue that optimality models can continue to be used, because when used properly they were never wedded to a global notion of optimization or rationality in the first place. Such models are best viewed as ways of finding out which of a pre-specified set of behavioural alternatives is likely to dominate in a specific environment. As such, they are important tools for a program of research into bounded rationality. The argument will be illustrated with examples from modelling work on social learning in rats and intentional communication in monkeys

Robot life: simulation and participation in the study of evolution and social behavior.

This paper explores the case of using robots to simulate evolution, in particular the case of Hamilton's Law. The uses of robots raises several questions that this paper seeks to address. The first concerns the role of the robots in biological research: do they simulate something (life, evolution, sociality) or do they participate in something? The second question concerns the physicality of the robots: what difference does embodiment make to the role of the robot in these experiments. Thirdly, how do life, embodiment and social behavior relate in contemporary biology and why is it possible for robots to illuminate this relation? These questions are provoked by a strange similarity that has not been noted before: between the problem of simulation in philosophy of science, and Deleuze's reading of Plato on the relationship of ideas, copies and simulacra

Bestial boredom: a biological perspective on animal boredom and suggestions for its scientific investigation

Boredom is likely to have adaptive value in motivating exploration and learning, and many animals may possess the basic neurological mechanisms to support it. Chronic inescapable boredom can be extremely aversive, and understimulation can harm neural, cognitive and behavioural flexibility. Wild and domesticated animals are at particular risk in captivity, which is often spatially and temporally monotonous. Yet biological research into boredom has barely begun, despite having important implications for animal welfare, the evolution of motivation and cognition, and for human dysfunction at individual and societal levels. Here I aim to facilitate hypotheses about how monotony affects behaviour and physiology, so that boredom can be objectively studied by ethologists and other scientists. I cover valence (pleasantness) and arousal (wakefulness) qualities of boredom, because both can be measured, and I suggest boredom includes suboptimal arousal and aversion to monotony. Because the suboptimal arousal during boredom is aversive, individuals will resist low arousal. Thus, behavioural indicators of boredom will, seemingly paradoxically, include signs of increasing drowsiness, alongside bouts of restlessness, avoidance and sensation-seeking behaviour. Valence and arousal are not, however, sufficient to fully describe boredom. For example, human boredom is further characterized by a perception that time ‘drags’, and this effect of monotony on time perception can too be behaviourally assayed in animals. Sleep disruption and some abnormal behaviour may also be caused by boredom. Ethological research into this emotional phenomenon will deepen understanding of its causes, development, function and evolution, and will enable evidence-based interventions to mitigate human and animal boredom

Rodent Biology and Management; Abstracts of papers presented at the International Conference on Rodent Biology and Management, held at Beijing, China, 5-9 October 1998

Farm Management,

Effects of the topology of social networks on information transmission

Social behaviours cannot be fully understood without considering the network structures that underlie them. Developments in network theory provide us with relevant modelling tools. The topology of social networks may be due to selection for information transmission. To investigate this, we generated network topologies with varying proportions of random connections and degrees of preferential attachment. We simulated two social tasks on these networks: a spreading innovation model and a simple market. Results indicated that non-zero levels of random connections and low levels of preferential attachment led to more efficient information transmission. Theoretical and practical implications are discussed

Ecologically-Based Management of Rodent Pests

Farm Management,

Aspiration Dynamics of Multi-player Games in Finite Populations

Studying strategy update rules in the framework of evolutionary game theory, one can differentiate between imitation processes and aspiration-driven dynamics. In the former case, individuals imitate the strategy of a more successful peer. In the latter case, individuals adjust their strategies based on a comparison of their payoffs from the evolutionary game to a value they aspire, called the level of aspiration. Unlike imitation processes of pairwise comparison, aspiration-driven updates do not require additional information about the strategic environment and can thus be interpreted as being more spontaneous. Recent work has mainly focused on understanding how aspiration dynamics alter the evolutionary outcome in structured populations. However, the baseline case for understanding strategy selection is the well-mixed population case, which is still lacking sufficient understanding. We explore how aspiration-driven strategy-update dynamics under imperfect rationality influence the average abundance of a strategy in multi-player evolutionary games with two strategies. We analytically derive a condition under which a strategy is more abundant than the other in the weak selection limiting case. This approach has a long standing history in evolutionary game and is mostly applied for its mathematical approachability. Hence, we also explore strong selection numerically, which shows that our weak selection condition is a robust predictor of the average abundance of a strategy. The condition turns out to differ from that of a wide class of imitation dynamics, as long as the game is not dyadic. Therefore a strategy favored under imitation dynamics can be disfavored under aspiration dynamics. This does not require any population structure thus highlights the intrinsic difference between imitation and aspiration dynamics