Data collected under ESRC transformative research award: "A new perspective on human judgement and decision making as optimal: A framework for behaviour change". Data comprise 6 experimental studies: 4 investigating human decision making under risk and 2 investigating human randomness perception. Decision making studies investigate behaviour in contextual preference reversal-type studies and provide data which is consistent with a Bayes-optimal rational model of choice. Randomness studies investigate human generation and judgement of random binary sequences and assess consistency with a theoretical account of randomness perception provided by Hahn & Warren, Psych Review, 2009.The dominant perspective in psychology and behavioural economics characterises human Judgement and Decision Making (JDM) as flawed. We propose a new examination of JDM starting from the premise that humans are optimal given the constraints imposed on them. We aim to demonstrate that if we take into account the constraints on human cognitive capacity or those imposed by the environment, human JDM is not flawed and is, in fact, optimal given those constraints. We will consider example phenomena, oft-cited as evidence of irrationality and measure both constraints and performance experimentally. We will then compare performance to that of optimal computational models subject to the same constraints. Further, we will show that if we change constraints, humans will adapt and remain optimal, providing a proof of principle of the potential power of this approach to bring about advantageous behavioural change via a principled framework for policy development. Because the current perspective has failed to deliver an account of the causal determinants of behaviour, our proposed theory promises a major shift in social science and behavioural economics research. We aim to displace influential descriptive theories of observed biases with a new framework allowing the development of predictive, falsifiable hypotheses of human JDM.

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