James A. R. Marshall

On optimal decision-making in brains and social insect colonies

The problem of how to compromise between speed and accuracy in decision-making faces organisms at many levels of biological complexity. Striking parallels are evident between decision-making in primate brains and collective decision-making in social insect colonies: in both systems, separate populations accumulate evidence for alternative choices; when one population reaches a threshold, a decision is made for the corresponding alternative, and this threshold may be varied to compromise between the speed and the accuracy of decision-making. In primate decision-making, simple models of these processes have been shown, under certain parametrizations, to implement the statistically optimal procedure that minimizes decision time for any given error rate. In this paper, we adapt these same analysis techniques and apply them to new models of collective decision-making in social insect colonies. We show that social insect colonies may also be able to achieve statistically optimal collective decision-making in a very similar way to primate brains, via direct competition between evidence-accumulating populations. This optimality result makes testable predictions for how collective decision-making in social insects should be organized. Our approach also represents the first attempt to identify a common theoretical framework for the study of decision-making in diverse biological systems.

On cost-efficiency of the global container shipping network

This paper presents a simple formulation in the form of a pipe network for modelling the global container-shipping network. The cost-efficiency and movement-patterns of the current container-shipping network have been investigated using heuristic methods. The model is able to reproduce the overall incomes, costs, and container movement patterns for the industry as well as for the individual shipping lines and ports. It was found that the cost of repositioning empties is 27% of the total world fleet running cost and that overcapacity continues to be a problem. The model is computationally efficient. Implemented in the Java language, it takes one minute to run a full-scale network on a Pentium IV computer.

Group selection and kin selection: formally equivalent approaches.

Inclusive fitness theory, summarised in Hamiltons rule, is a dominant explanation for the evolution of social behaviour. A parallel thread of evolutionary theory holds that selection between groups is also a candidate explanation for social evolution. The mathematical equivalence of these two approaches has long been known. Several recent papers, however, have objected that inclusive fitness theory is unable to deal with strong selection or with non-additive fitness effects, and concluded that the group selection framework is more general, or even that the two are not equivalent after all. Yet, these same problems have already been identified and resolved in the literature. Here, I survey these contemporary objections, and examine them in the light of current understanding of inclusive fitness theory.

Mammalian choices: combining fast-but-inaccurate and slow-but-accurate decision-making systems

Empirical findings suggest that the mammalian brain has two decision-making systems that act at different speeds. We represent the faster system using standard signal detection theory. We represent the slower (but more accurate) cortical system as the integration of sensory evidence over time until a certain level of confidence is reached. We then consider how two such systems should be combined optimally for a range of information linkage mechanisms. We conclude with some performance predictions that will hold if our representation is realistic.

Decision-making under uncertainty: biases and Bayesians

Animals (including humans) often face circumstances in which the best choice of action is not certain. Environmental cues may be ambiguous, and choices may be risky. This paper reviews the theoretical side of decision-making under uncertainty, particularly with regard to unknown risk (ambiguity). We use simple models to show that, irrespective of pay-offs, whether it is optimal to bias probability estimates depends upon how those estimates have been generated. In particular, if estimates have been calculated in a Bayesian framework with a sensible prior, it is best to use unbiased estimates. We review the extent of evidence for and against viewing animals (including humans) as Bayesian decision-makers. We pay particular attention to the Ellsberg Paradox, a classic result from experimental economics, in which human subjects appear to deviate from optimal decision-making by demonstrating an apparent aversion to ambiguity in a choice between two options with equal expected rewards. The paradox initially seems to be an example where decision-making estimates are biased relative to the Bayesian optimum. We discuss the extent to which the Bayesian paradigm might be applied to the evolution of decision-makers and how the Ellsberg Paradox may, with a deeper understanding, be resolved.

Swarm Cognition: an Interdisciplinary Approach to the study of Self-organising Biological Collectives

Basic elements of cognition have been identified in the behaviour displayed by animal collectives, ranging from honeybee swarms to human societies. For example, an insect swarm is often considered a “super-organism” that appears to exhibit cognitive behaviour as a result of the interactions among the individual insects and between the insects and the environment. Progress in disciplines such as neurosciences, cognitive psychology, social ethology and swarm intelligence has allowed researchers to recognise and model the distributed basis of cognition and to draw parallels between the behaviour of social insects and brain dynamics. In this paper, we discuss the theoretical premises and the biological basis of Swarm Cognition, a novel approach to the study of cognition as a distributed self-organising phenomenon, and we point to novel fascinating directions for future work.

A simple threshold rule is sufficient to explain sophisticated collective decision-making.

Decision-making animals can use slow-but-accurate strategies, such as making multiple comparisons, or opt for simpler, faster strategies to find a ‘good enough’ option. Social animals make collective decisions about many group behaviours including foraging and migration. The key to the collective choice lies with individual behaviour. We present a case study of a collective decision-making process (house-hunting ants, Temnothorax albipennis), in which a previously proposed decision strategy involved both quality-dependent hesitancy and direct comparisons of nests by scouts. An alternative possible decision strategy is that scouting ants use a very simple quality-dependent threshold rule to decide whether to recruit nest-mates to a new site or search for alternatives. We use analytical and simulation modelling to demonstrate that this simple rule is sufficient to explain empirical patterns from three studies of collective decision-making in ants, and can account parsimoniously for apparent comparison by individuals and apparent hesitancy (recruitment latency) effects, when available nests differ strongly in quality. This highlights the need to carefully design experiments to detect individual comparison. We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out. However, by using a simple threshold rule, decision-making groups are able to effectively compare options, without relying on any form of direct comparison of alternatives by individuals. This parsimonious mechanism could promote collective rationality in group decision-making.

On evolutionary explanations of cognitive biases

Apparently irrational biases such as overconfidence, optimism, and pessimism are increasingly studied by biologists, psychologists, and neuroscientists. Functional explanations of such phenomena are essential; we argue that recent proposals, focused on benefits from overestimating the probability of success in conflicts or practising self-deception to better deceive others, are still lacking in crucial regards. Attention must be paid to the difference between cognitive and outcome biases; outcome biases are suboptimal, yet cognitive biases can be optimal. However, given that cognitive biases are subjectively experienced by affected individuals, developing theory and collecting evidence on them poses challenges. An evolutionary theory of cognitive bias might require closer integration of function and mechanism, analysing the evolution of constraints imposed by the mechanisms that determine behaviour.

A Mechanism for Value-Sensitive Decision-Making

We present a dynamical systems analysis of a decision-making mechanism inspired by collective choice in house-hunting honeybee swarms, revealing the crucial role of cross-inhibitory ‘stop-signalling’ in improving the decision-making capabilities. We show that strength of cross-inhibition is a decision-parameter influencing how decisions depend both on the difference in value and on the mean value of the alternatives; this is in contrast to many previous mechanistic models of decision-making, which are typically sensitive to decision accuracy rather than the value of the option chosen. The strength of cross-inhibition determines when deadlock over similarly valued alternatives is maintained or broken, as a function of the mean value; thus, changes in cross-inhibition strength allow adaptive time-dependent decision-making strategies. Cross-inhibition also tunes the minimum difference between alternatives required for reliable discrimination, in a manner similar to Webers law of just-noticeable difference. Finally, cross-inhibition tunes the speed-accuracy trade-off realised when differences in the values of the alternatives are sufficiently large to matter. We propose that the model, and the significant role of the values of the alternatives, may describe other decision-making systems, including intracellular regulatory circuits, and simple neural circuits, and may provide guidance in the design of decision-making algorithms for artificial systems, particularly those functioning without centralised control.

Social evolution and inclusive fitness theory : an introduction

Social behavior has long puzzled evolutionary biologists, since the classical theory of natural selection maintains that individuals should not sacrifice their own fitness to affect that of others. Social Evolution and Inclusive Fitness Theory argues that a theory first presented in 1963 by William D. Hamilton—inclusive fitness theory—provides the most fundamental and general explanation for the evolution and maintenance of social behavior in the natural world. James Marshall guides readers through the vast and confusing literature on the evolution of social behavior, introducing and explaining the competing theories that claim to provide answers to questions such as why animals evolve to behave altruistically. Using simple statistical language and techniques that practicing biologists will be familiar with, he provides a comprehensive yet easily understandable treatment of key concepts and their repeated misinterpretations. Particular attention is paid to how more realistic features of behavior, such as nonadditivity and conditionality, can complicate analysis. Marshall highlights the general problem of identifying the underlying causes of evolutionary change, and proposes fruitful approaches to doing so in the study of social evolution. Social Evolution and Inclusive Fitness Theory describes how inclusive fitness theory addresses both simple and complex social scenarios, the controversies surrounding the theory, and how experimental work supports the theory as the most powerful explanation for social behavior and its evolution.