Publications

If we reassess the rationality question under the assumption that the uncertainty of the natural world is largely unquantifiable, where do we end up? In this article I argue that we arrive at a statistical, normative, and cognitive theory of ecological rationality. The main casualty of this rebuilding process is optimality. Once we view optimality as a formal implication of quantified uncertainty rather than an ecologically meaningful objective, the rationality question shifts from being axiomatic/probabilistic in nature to being algorithmic/predictive in nature. These distinct views on rationality mirror fundamental and long-standing divisions in statistics.

Brighton, H. (2019)

Economics Discussion Papers, No 2019-25, Kiel Institute for the World Economy [Open for public peer review at Economics E-Journal].

Ecological rationality provides an alternative to the view that rational responses to environmental uncertainty are optimal probabilistic responses. In this article I respond recent critiques by clarifying that ecological rationality is more than a set of algorithmic conjectures. It is also driven by statistical commitments governing the treatment of unquantifiable uncertainty.

Brighton, H. (2018)

Preprint: PsyArXiv (To appear in: Routledge Handbook of Bounded Rationality)

Public risk perception of hazardous events such as contagious outbreaks, terrorist attacks, and climate change are difficult-to- anticipate social phenomena. It is unclear how risk information will spread through social networks, how laypeople influence each other, and what social dynamics generate public opinion. In this article we examine how messages detailing risks are transmitted from one person to another in experimental diffusion chains and how people influence each other as they propagate this information.

Moussaïd, M., Brighton, H., & Gaissmaier, W. (2015)

Proceedings of the National Academy of Sciences of the USA, 112(18), 5631-5636.

Surprisingly simple models sometimes predict more accurately than more complex, sophisticated models. Here, we address the question of when and why simple models succeed — or fail — by framing the forecasting problem in terms of the bias–variance dilemma. Controllable error in forecasting consists of two components, the “bias” and the “variance”. We argue that the benefits of simplicity are often overlooked because of a pervasive “bias bias”.

Brighton, H. & Gigerenzer, G. (2015)

Journal of Business Research, 68, 1772-1784.

In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, com- putation, and time can in fact improve accuracy. We review the major progress made so far and argue that Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies.

Gigerenzer, G. & Brighton, H. (2009)

Topics in Cognitive Science, 1, 107-143.

We show how cultural selection for learnability during the process of linguistic evolution can be visualized using a simple iterated learning model. Computational models of linguistic evolution typically focus on the nature of, and conditions for, stable states. We take a novel approach and focus on understanding the process of linguistic evolution itself.

Brighton, H. & Kirby, S. (2006)

Artificial Life, 12, 229-242.

In this article we develop the theory that language codes information about it’s own structure, and as a result, languages themselves provide information that influences their own survival. To understand the consequences of this theory we discuss recent computational models of linguistic evolution. Linguistic evolution is the process by which languages themselves evolve.

Brighton, H. Kirby, S. & Smith, K. (2005)

Physics of Life Reviews, 2, 177-226.

Can machines really think? Is the mind just a complicated computer program? Introducing Artificial Intelligence focuses on the major issues behind one of the hardest scientific problems ever undertaken. Half a century of research into the construction of intelligent machinery has resulted in machines capable of beating the best human chess players and humanoid robots that can walk and interact with us. How exactly should we go about building an intelligent machine? Should it work like a mind? Should it work like a brain? Does it require a body?

Brighton, H., & Selina, H. (2003)

Cambridge: Icon Books

Knowledge of language is complex, yet the data available to the language learner are sparse. This incongruous situation, termed the “poverty of the stimulus,” is accounted for by placing much of the specification of language in the LAD. The assumption is that the characteristic structure of language is somehow coded genetically. The effect of language evolution on the cultural substrate, in the absence of genetic change, is not addressed by this explanation. We show that the poverty of the stimulus introduces a pressure for compositional language structure when we consider language evolution resulting from iterated observational learning.

Brighton, H. (2002)

Artificial Life, 8, 25-54.