London Judgement and Decision Making seminars
The LJDM seminar series is supported by
University College London
City University London
Originally established at UCL in the early 1970’s as a weekly Cognition and Reasoning seminar, it later became an intercollegiate seminar on Language and Cognition in the early 1980’s.
The name LJDM was finally coined in 1990, and the group has been running seminars under this name ever since, with lecturers and researchers in and around the UK meeting on a regular basis to discuss judgment and decision making, judgments of likelihood, reasoning, thinking, problem solving, forecasting, risk perception and communication, and other related topics.
If you would like to present your research to the group or to suggest a speaker, please contact the organizers:
- Lara Kirfel (email@example.com),
- Sabine Topf (firstname.lastname@example.org), and
- Tamara Shengelia (email@example.com)
Unless specified otherwise, all seminars take place on Wednesdays at 5pm, in Room 313 at the Psychology Department, University College London (on the corner of Bedford Way, Gordon Square and Torrington Place, London WC1H 0AP). Map.
To get updates on the current schedule and weekly reminders of the seminars, please subscribe to the Risk and Decision mailing list.
All are welcome to attend.
Term 1 Seminar Schedule
October – December 2017
4th October 2017
Predicting Judicial Decisions of the European Court of Human Rights
Recent advances in Natural Language Processing and Machine Learning provide us with the tools to build predictive models that can be used to unveil patterns driving judicial decisions. This can be useful, for both lawyers and judges, as an assisting tool to rapidly identify cases and extract patterns which lead to certain decisions. This paper presents the first systematic study on predicting the outcome of cases tried by the European Court of Human Rights based solely on textual content. We formulate a binary classification task where the input of our classifiers is the textual content extracted from a case and the target output is the actual judgment as to whether there has been a violation of an article of the convention of human rights. Textual information is represented using contiguous word sequences, i.e., N-grams, and topics. Our models can predict the court’s decisions with a strong accuracy (79% on average). Our empirical analysis indicates that the formal facts of a case are the most important predictive factor. This is consistent with the theory of legal realism suggesting that judicial decision-making is significantly affected by the stimulus of the facts. We also observe that the topical content of a case is another important feature in this classification task and explore this relationship further by conducting a qualitative analysis.
11th October 2017
Transactional cognition: The constitutive yet invisible role of things in human cognitive performance
Kingston University London
Higher cognition involved in reasoning, problem-solving, and decision-making is admittedly conceived as a process of acquiring information and understanding through mental representation and mental computation. Some have conceded cognition does not take place in a vacuum: it can also be shaped by our bodily sensations and by our immediate environment. This prompted modified conceptions of cognition tagged "embodied" or "situated". Radicals have philosophised that cognition is a-representational and a-computational: it would only emerge from action-environment dynamics. While these ideas have been around for well over two decades, they have had minimal impact on mainstream cognitive psychology. In this talk, I will present data on Bayesian reasoning which challenges both the traditional, modified and the radical conception of cognition and discuss a fourth avenue: cognition as emerging through time and space from a transactional process meshing mental and physical representations, computations, as well as actions on things.
Link to relevant papers
18th October 2017
Parametric estimation of social preference models
Peter G Moffatt
University of East Anglia
We analyse data on dictator game giving from an experiment in which the price of giving is varied. We use this data set to estimate a number of different models, each based on a utility function with own-payoff and other’s payoff as arguments. We are particularly interested in the well-known Fehr-Schmidt utility function. A fundamental problem with this utility function is non-differentiability, which leads to the solution to the constrained optimisation problem being either one of extreme egalitarianism or extreme selfishness. We overcome this problem by introducing a parameter to the utility function that allows strict quasi-concavity, and therefore allows non-corner solutions. The Fehr-Schmidt function is a limiting case of our extended function. We estimate the extended model by MSL, allowing between-subject heterogeneity in both of the Fehr-Schmidt parameters (aversion to disadvantageous and advantageous inequality). We find substantial heterogeneity in both of these.
25th October 2017
Joint Session with the LSE Choice Group
Deliberative account of causation
University of Warwick
Fundamental physics makes no clear use of causal notions; it uses laws that operate in relevant respects in both temporal directions and that relate whole systems across times. But by relating causation to evidence, we can explain how causation fits in to a physical picture of the world and explain its temporal asymmetry. This paper presents a deliberative account of causation, according to which causal relations correspond to the evidential relations we need when we decide on one thing in order to achieve another. Tamsin’s taking her umbrella is a cause of her staying dry, for example, if and only if her deciding to take her umbrella for the sake of staying dry is adequate grounds for believing she’ll stay dry. This correspondence explains why causation matters: knowledge of causal structure helps us make decisions that are evidence of outcomes we seek. The account also explains why we can control the future and not the past, and why causes come before their effects. When agents properly deliberate, their decisions can never count as evidence for any outcomes they may seek in the past. From this it follows that causal relations don’t run backwards. This deliberative asymmetry is itself traced back to asymmetries of evidence and entropy, providing a new way of deriving causal asymmetry from temporally symmetric laws.
1st November 2017
Opinion and belief revision in social situations
Birkbeck, University of London
In my research I examine how and why we change our opinions. In this talk I will report on a set of experiments that have been conducted on belief revision in a group setting with the aim of inferring the rules that individuals use when revising their beliefs in light of more information. I will explore various factors that influence the likelihood of belief revision, different strategies that participants use when revising their beliefs and various existing models that predict belief revision. Finally, I will explore the role of confidence, distance to the mean, falling inside or outside of the group consensus, among other explanatory variables and present a new model of belief revision that predicts individual revision with more accuracy than the existing models.
8th November 2017
No seminar in reading week
15th November 2017
Adaptive Decisions in Deceptive Environments
University of Huddersfield
There are two things we can say we know about people’s ability to detect lies: (i) they hit around chance accuracy, and (ii) they are biased to believe others are telling the truth. Unsurprisingly, the field has taken a rather pessimistic view of lie detectors, describing them as naive, gullible, and without control over their decision process. The Adaptive Lie Detector theory (ALIED: www.conflictlab.org/alied) takes a more optimistic view. It argues that accuracy is low because there is little diagnostic information available. In the absence of useful cues to deception, people can rely on context-generalised past experience to make an ‘educated guess’. This talk will demonstrate a number of studies in support of the account and consider its practical implications for lie detection.
22th November 2017
When there’s no other explanation: What makes an effective correction?
City, University of London
From rumours that genetically modified mosquitoes caused the spread of the Zika virus, to misleading claims emblazoned on the side of a bus that leaving the EU would save the NHS £350 million, people are inundated with misinformation. False or inaccurate claims are that have even been corrected or thoroughly discredited (e.g., evidence that vaccines do not cause autism), can pervasively influence us. Research on the continued influence effect of misinformation, has consistently shown that corrected misinformation has a persistent effect on memory and reasoning. We investigated whether “explanatory” corrections, explaining why misinformation should no longer be believed, are better at counteracting misinformation than non-explanatory corrections. Over three experiments, we find evidence that explanatory and non-explanatory are equally effective at reducing the continued influence effect . We discuss the cognitive mechanisms involved in correcting misinformation and the implications for communication.
29th November 2017
Your Brain is Built for Poltics
University of Exeter
The book argues that Your Brain is Built for Politics, drawing from an extensive body of research in biology and politics. Negotiating increasingly complex and shifting coalitions drove the human brain to evolve a set of mechanisms that modern humans now engage when they participate in national politics. The book synthesizes results from six brain imaging experiments, a large-n response latency study, and a computational model of the visual cortex to explore how these brain mechanisms underpin phenomena such as political sophistication, political attitudes, racial attitudes, and moral reasoning. Predictions of party affiliation with 82% accuracy, election results with 65-75% accuracy, and both egalitarian attitudes and behaviors are achieved with surprisingly simple models accounting for brain function. The product is a new view of human nature. Biology is shown to be subservient to the demands of human politics. Rather than a reductionist or deterministic argument, I contend the shifting coalitions of human society require that we are hardwired to not be hardwired.
6th December 2017
An Adaptive Nature of Developmental Limitations
Ohio State University
Childhood is often construed as period of developmental limitations: in almost every aspect of human functioning older children and adults outperform younger participants. However, childhood is also the time of unique opportunities in terms of learning new things. In this talk, I present new findings demonstrating how children’s limitations in cognitive control, planning, and executive function result in children outperforming adults on attention and memory tasks. I then discuss an adaptive nature of these limitations and argue that they allow optimizing exploration, something that is necessary for successful learning and cognitive development.
13th December 2017
Human risk-reward trade-offs: adaptive and (partly) domain-general
University of Oxford
When selecting amongst actions, humans and other animals must trade-off risks and rewards. Humans exhibit remarkably consistent and stereotyped deviations from optimality when choosing between described actions – the canonical experimental paradigm. More recently, two separate literatures have undermined the view that canonical results reflect general properties of human choice. When risks and rewards are experienced rather than described, or when making decisions reliant upon our sensory systems, choices appear radically different. These dissociations imply highly domain-specific mechanisms. However, it is unclear whether dissociations arise due to subtle differences between paradigms, context-specific risk attitudes, or genuinely different mechanisms. To directly assess domain-specificity, we borrowed an analytical tool from learning paradigms and evaluate the extent to which learning transfers across risk domains. In an extensive double-blind randomized control trial, we trained people to make better decisions in one domain and test for transfer to other risk domains. We find that people tune their trade-offs to make better decisions – showing that risk attitudes are not fixed as the canonical findings imply – but highly adaptive. Importantly, we observe partial transfer of learning, ruling out strong domain specificity. Time permitting, I will also discuss work addressing the description-experience gap specifically.