Behavioral Law and Economics

Behavioral economics has been a growing force in many fields of applied economics, including public economics, labor economics, health economics, and law and economics. This paper describes and assesses the current state of behavioral law and economics. Law and economics had a critical (though underrecognized) early point of contact with behavioral economics through the foundational debate in both fields over the Coase theorem and the endowment effect. In law and economics today, both the endowment effect and other features of behavioral economics feature prominently and have been applied in many important legal domains. The paper concludes with reference to a new emphasis in behavioral law and economics on "debiasing through law" - using existing or proposed legal structures in an attempt to reduce people's departures from the traditional economic assumption of unbounded rationality.

Demographic Inference and Representative Population Estimates from Multilingual Social Media Data

Social media provide access to behavioural data at an unprecedented scale and granularity. However, using these data to understand phenomena in a broader population is difficult due to their non-representativeness and the bias of statistical inference tools towards dominant languages and groups. While demographic attribute inference could be used to mitigate such bias, current techniques are almost entirely monolingual and fail to work in a global environment. We address these challenges by combining multilingual demographic inference with post-stratification to create a more representative population sample. To learn demographic attributes, we create a new multimodal deep neural architecture for joint classification of age, gender, and organization-status of social media users that operates in 32 languages. This method substantially outperforms current state of the art while also reducing algorithmic bias. To correct for sampling biases, we propose fully interpretable multilevel regression methods that estimate inclusion probabilities from inferred joint population counts and ground-truth population counts. In a large experiment over multilingual heterogeneous European regions, we show that our demographic inference and bias correction together allow for more accurate estimates of populations and make a significant step towards representative social sensing in downstream applications with multilingual social media.Comment: 12 pages, 10 figures, Proceedings of the 2019 World Wide Web Conference (WWW '19

Behavioural analytics: Exploring judgments and choices in large data sets

The ever-increasing availability of large data-sets that store users’ judgements (such as forecasts and preferences) and choices (such as acquisitions of goods and services) provides a fertile ground for Behavioural Operational Research (BOR). In this paper, we review the streams of Behavioural Decision Research that might be useful for BOR researchers and practitioners to analyse such behavioural data-sets. We then suggest ways that concepts from these streams can be employed in exploring behavioural data-sets for (i) detecting behavioural patterns, (ii) exploiting behavioural findings and (iii) improving judgements and decisions of consumers and citizens. We also illustrate how this taxonomy for behavioural analytics might be utilised in practice, in three real-world studies with behavioural data-sets generated by websites and online user activity

Debiasing Decisions. Improved Decision Making With A Single Training Intervention

From failures of intelligence analysis to misguided beliefs about vaccinations, biased judgment and decision making contributes to problems in policy, business, medicine, law, and private life. Early attempts to reduce decision biases with training met with little success, leading scientists and policy makers to focus on debiasing by using incentives and changes in the presentation and elicitation of decisions. We report the results of two longitudinal experiments that found medium to large effects of one-shot debiasing training interventions. Participants received a single training intervention, played a computer game or watched an instructional video, which addressed biases critical to intelligence analysis (in Experiment 1: bias blind spot, confirmation bias, and fundamental attribution error; in Experiment 2: anchoring, representativeness, and social projection). Both kinds of interventions produced medium to large debiasing effects immediately (games ≥ -31.94% and videos ≥ -18.60%) that persisted at least 2 months later (games ≥ -23.57% and videos ≥ -19.20%). Games, which provided personalized feedback and practice, produced larger effects than did videos. Debiasing effects were domain-general: bias reduction occurred across problems in different contexts, and problem formats that were taught and not taught in the interventions. The results suggest that a single training intervention can improve decision making. We suggest its use alongside improved incentives, information presentation, and nudges to reduce costly errors associated with biased judgments and decisions

Cognitive debiasing 2: Impediments to and strategies for change

In a companion paper, we proposed that cognitive debiasing is a skill essential in developing sound clinical reasoning to mitigate the incidence of diagnostic failure. We reviewed the origins of cognitive biases and some proposed mechanisms for how debiasing processes might work. In this paper, we first outline a general schema of how cognitive change occurs and the constraints that may apply. We review a variety of individual factors, many of them biases themselves, which may be impediments to change. We then examine the major strategies that have been developed in the social sciences and in medicine to achieve cognitive and affective debiasing, including the important concept of forcing functions. The abundance and rich variety of approaches that exist in the literature and in individual clinical domains illustrate the difficulties inherent in achieving cognitive change, and also the need for such interventions. Ongoing cognitive debiasing is arguably the most important feature of the critical thinker and the well-calibrated mind. We outline three groups of suggested interventions going forward: educational strategies, workplace strategies and forcing functions. We stress the importance of ambient and contextual influences on the quality of individual decision making and the need to address factors known to impair calibration of the decision maker. We also emphasise the importance of introducing these concepts and corollary development of training in critical thinking in the undergraduate level in medical education

The Behavioral Paradox: Why Investor Irrationality Calls for Lighter and Simpler Financial Regulation

It is widely believed that behavioral economics justifies more intrusive regulation of financial markets, because people are not fully rational and need to be protected from their quirks. This Article challenges that belief. Firstly, insofar as people can be helped to make better choices, that goal can usually be achieved through light-touch regulations. Secondly, faulty perceptions about markets seem to be best corrected through market-based solutions. Thirdly, increasing regulation does not seem to solve problems caused by lack of market discipline, pricing inefficiencies, and financial innovation; better results may be achieved with freer markets and simpler rules. Fourthly, regulatory rule makers are subject to imperfect rationality, which tends to reduce the quality of regulatory intervention. Finally, regulatory complexity exacerbates the harmful effects of bounded rationality, whereas simple and stable rules give rise to positive learning effects

Regulating Complacency: Human Limitations and Legal Efficacy

This Article examines how insights into limited human rationality can improve financial regulation. The Article identifies four categories of limitations—herd behavior, cognitive biases, overreliance on heuristics, and a proclivity to panic—that undermine the perfect-market regulatory assumptions that parties have full information and will act in their rational self-interest. The Article then analyzes how insights into these limitations can be used to correct resulting market failures. Requiring more robust disclosure and due diligence, for example, can help to reduce reliance on misleading information cascades that motivate herd behavior. Debiasing through law, such as requiring more specific, poignant, and concrete disclosure of risks and their consequences, can help to correct cognitive biases. Requiring firms to engage in more self-aware operational risk management and reporting can reduce the likelihood that parties will over-rely on heuristics. And legislating backstop market liquidity and other stabilizing controls can help to minimize panics. Regulation, however, can only partly overcome these limitations. Effective financial regulation should therefore be designed not only to address these limitations but also to try to mitigate the harm of inevitable financial failures

Is it time for studying real-life debiasing? Evaluation of the effectiveness of an analogical intervention technique.

The aim of this study was to initiate the exploration of debiasing methods applicable in real-life settings for achieving lasting improvement in decision making competence regarding multiple decision biases. Here, we tested the potentials of the analogical encoding method for decision debiasing. The advantage of this method is that it can foster the transfer from learning abstract principles to improving behavioral performance. For the purpose of the study, we devised an analogical debiasing technique for 10 biases (covariation detection, insensitivity to sample size, base rate neglect, regression to the mean, outcome bias, sunk cost fallacy, framing effect, anchoring bias, overconfidence bias, planning fallacy) and assessed the susceptibility of the participants (N = 154) to these biases before and 4 weeks after the training. We also compared the effect of the analogical training to the effect of 'awareness training' and a 'no-training' control group. Results suggested improved performance of the analogical training group only on tasks where the violations of statistical principles are measured. The interpretation of these findings require further investigation, yet it is possible that analogical training may be the most effective in the case of learning abstract concepts, such as statistical principles, which are otherwise difficult to master. The study encourages a systematic research of debiasing trainings and the development of intervention assessment methods to measure the endurance of behavior change in decision debiasing

Regulating Complacency: Human Limitations and Legal Efficacy

This Article examines how insights into limited human rationality can improve financial regulation. The Article identifies four categories of limitations—herd behavior, cognitive biases, overreliance on heuristics, and a proclivity to panic—that undermine the perfect-market regulatory assumptions that parties have full information and will act in their rational self-interest. The Article then analyzes how insights into these limitations can be used to correct resulting market failures. Requiring more robust disclosure and due diligence, for example, can help to reduce reliance on misleading information cascades that motivate herd behavior. Debiasing through law, such as requiring more specific, poignant, and concrete disclosure of risks and their consequences, can help to correct cognitive biases. Requiring firms to engage in more self-aware operational risk management and reporting can reduce the likelihood that parties will over-rely on heuristics. And legislating backstop market liquidity and other stabilizing controls can help to minimize panics. Regulation, however, can only partly overcome these limitations. Effective financial regulation should therefore be designed not only to address these limitations but also to try to mitigate the harm of inevitable financial failures