Research Themes

The goal of the EUCAS Lab is to advance the understanding of cognitive and adaptive systems by examining how theories, models, and organisms cope with extended uncertainty conditions.

Theories, models, and information processing problems assume or imply a set of operating conditions. These conditions can often reflect simplifying or formally convenient assumptions that bear little relation to the properties of uncertain natural environments. The study of extended uncertainty examines the implications of imposing further uncertainties on these operating conditions. For example, rational decisions are typically defined in terms of known probabilities, utilities, actions, and consequences. One of the central concerns for the EUCAS Lab is how the notion of rationality might change when we introduce, for example, unquantifiable, Knightian uncertainty. In the following, we elaborate on this and other research themes that conern the EUCAS Lab.

Theme 1: Ecological Rationality

How does the statistical problem facing the organism change when we can’t fully quantify environmental uncertainty? The study of ecological rationality investigates rationality in worlds where deep uncertainty precludes the ability to formulate optimal probabilistic responses.

“it is sometimes more rational to admit that one does not have sufficient information for probabilistic beliefs than to pretend that one does”
(Gilboa et al., 2012, p. 27)

Given a formally well-defined task, a rational actor model defines a rationally justified, optimal response. Rational actor models are desirable goals in the behavioral, cognitive, and social sciences, but in some contexts the distinction between small and large worlds can be used to question the cachet associated with the terms “rational” and “optimal.” Ideally suited to the analysis of small world problems, both concepts can be counterproductive in the analysis of large world problems. In small worlds, the relevant problem characteristics are certain and uncontroversial in their formalization. Large worlds are characterized by inherent uncertainty and ignorance, properties which undermine the validity and existence of optimal responses.

Key Publications

  • Brighton, H. (2019). Beyond quantified ignorance: Rebuilding rationality without the bias bias. Economics Discussion Papers, No 2019-25, Kiel Institute for the World Economy [Open for public peer review at Economics E-Journal].
  • Brighton, H. (2018). Rationality without optimality: Bounded and ecological rationality from a Marrian perspective. PsyArXiv (To appear in: Routledge Handbook of Bounded Rationality)
  • Todd, P. M., & Brighton, H. (2016). Building the theory of ecological rationality. Minds and Machines.
  • Brighton, H. & Gigerenzer, G. (2012) Are rational actor models "rational" outside small worlds? In: S. Okasha, & K. Binmore (Eds.) Evolution and Rationality: Decisions, Co-operation, and Strategic Behaviour (pp. 84-109). Cambridge: Cambridge University Press.
  • Brighton, H. & Todd, P. M. (2008) Situating rationality: Ecologically rational decision making with simple heuristics In: P. Robbins & M. Aydede (Eds.) Cambridge handbook of situated cognition (pp. 322-346). Cambridge: Cambridge University Press.

Theme 2: Decision Making and Simple Heuristics

How can we make good decisions in an uncertain world? The EUCAS Lab investigates the role of simple heuristics in answering this question. Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy.

“How do human beings reason when the conditions for rationality postulated by the model of neoclassical economics are not met — for example, when no one can define the appropriate utility function”? (Simon, 1989, p. 377)

The study of simple heuristics considers: (a) the discovery of less-is-more effects; (b) the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; (c) an advancement from vague labels to computational models of heuristics; (d) the development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an ‘‘adaptive toolbox;’’ and (e) the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people’s adaptive use of heuristics.

Key Publications

  • Brighton, H. & Gigerenzer, G. (2015). The bias bias. Journal of Business Research, 68, 1772-1784.
  • Brighton, H. & Gigerenzer, G. (2011). Towards competitive instead of biased testing of heuristics. Topics in Cognitive Science, 3, 197-205.
  • Gigerenzer, G. & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1, 107-143.
  • Brighton, H. & Gigerenzer, G. (2008) Bayesian brains and cognitive mechanisms: Harmony or dissonance? In: N. Chater & M. Oaksford (Eds.) The Probabilistic Mind: Prospects for Bayesian Cognitive Science (pp. 189-208). Oxford: Oxford University Press

Theme 3: The Iterated Learning Model

What are implications of viewing the process of language acqusition as uncertain, and involving inductive generalizatons? One implication, which we have investigated by developing the Iterated Learning Model (ILM), is that language undergoes cultural selection for learnability. Using ILMs we have examined the theory that many of the hallmarks of language are adaptations to problem of repeated cultural trasnmission.

What are the implications of deviating from Chomsky’s position that knowledge of language goes “far beyond the presented primary linguistic data and is in no sense an ‘inductive generalisation’ from these data” (Chomsky, 1965, p. 33).

Languages themselves provide information that influences their own survival. To understand the consequences of this theory we have developed computational models of linguistic evolution. Linguistic evolution is the process by which languages themselves evolve. Our research into linguistic evolution highlights the significance of this process in understanding the evolution of linguistic complexity. Our conclusions are that: (1) the process of linguistic transmission constitutes the basis for an evolutionary system, and (2), that this evolutionary system is only superficially comparable to the process of biological evolution.

Key Publications

  • Brighton, H. & Kirby, S. (2006). Understanding linguistic evolution by visualising the emergence of topographic mappings. Artificial Life, 12, 229-242.
  • Brighton, H. Kirby, S. & Smith, K. (2005). Language as an evolutionary system. Physics of Life Reviews, 2, 177-226.
  • Smith, K. Kirby, S. & Brighton, H. (2003). Iterated learning: A framework for the emergence of language. Artificial Life, 9, 371-386.
  • Brighton, H. (2002). Compositional syntax from cultural transmission. Artificial Life, 8, 25-54.

Theme 4. The Social Transmission of Risk

The iterated learning model considers how learned mappings between meanings and signals undergo change when culturally transmitted. What other aspects of cognition and behavior can be viewed in these terms? One related area of research for the EUCAS Lab is understanding how our perceptions of risk undergo change in response to repeated cultural transmission.

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. Our research has examined 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. Although the content of messages is gradually lost over repeated social transmissions, subjective perceptions of risk propagate and amplify due to social influence. These results provide quantitative insights into the public response to risk and the formation of often unnecessary fears and anxieties.

Key Publications

  • Moussaïd, M., Brighton, H., & Gaissmaier, W. (2015). The amplification of risk in experimental diffusion chains. Proceedings of the National Academy of Sciences of the USA, 112(18), 5631-5636.

References

  • Chomsky, N. (1965). Aspects of the theory of syntax. Cambridge, MA: MIT Press.
  • Simon, H. A. (1989). The scientist as problem solver. In Klahr, D. and Kotovsky, K., editors, Complex Information Processing: The Impact of Herbert A. Simon, pages 373-398. Erlbaum, Hillsdale, NJ.
  • Gilboa, I., Postlewaite, A., and Schmeidler, D. (2012). Rationality of belief or: Why Savage's axioms are neither necessary or sufficient for rationality. Synthese, 187, 11-31.