Finance seminars

Classical optimal transport assumes fully specified probability distributions. This talk relaxes that requirement by representing partially known marginals with belief functions. It examines joint belief functions with fixed marginals and minimizes a Choquet integral of transport cost. Preferences use an α-maxmin criterion, with α between zero and one measuring pessimism. When the cost is a ground metric, the construction gives a Choquet–Wasserstein pseudo-distance for comparing belief functions on a common space. The same framework describes games under ambiguity through a Stackelberg–Cournot–Nash equilibrium with Dempster–Shafer uncertainty and α-maxmin preferences. An application considers a continuum of investors whose type distribution is only partly known: investors choose portfolios of assets while a market maker sets prices.

This panel examines the growing need to account for nature and biodiversity in private-sector decisions. Researchers and business practitioners consider the challenges and opportunities involved, in the context of the IPBES assessment of biodiversity and business. The discussion brings perspectives from biodiversity science, management, financial disclosure, industrial leadership and water infrastructure to the relationship between ecosystems and commercial activity.

This panel examines how biodiversity science can inform decisions in business, finance and government. It assesses what is known about biodiversity status and trends in marine and terrestrial systems, which dimensions matter for decisions, and how to navigate financial-disclosure frameworks, science-based targets, national indicators and biodiversity credits. The discussion considers emerging methods including genomics, AI-based monitoring, functional traits, multiple knowledge systems and global indices. It asks how these approaches can support ecological recovery without reinforcing extractive measurement or governance, and how metrics can respect Indigenous rights, knowledge and decision-making. The panel also recognises that better measurement is only one component of changing business impacts on nature.

Recent advances in artificial intelligence have led to new forms of misinformation, including highly realistic “deepfake” synthetic media. We conduct three experiments to investigate how and why retail investors react to deepfake financial news. Results from the first two experiments provide evidence that investors use a “realism heuristic,” responding more intensely to audio and video deepfakes as their perceptual realism increases. In the third experiment, we introduce an intervention to prompt analytical thinking, varying whether participants make analytical judgments about credibility or intuitive investment judgments. When making intuitive investment judgments, investors are strongly influenced by both more and less realistic deepfakes. When making analytical credibility judgments, investors are able to discern the non-credibility of less realistic deepfakes but struggle with more realistic deepfakes. Thus, while analytical thinking can reduce the impact of less realistic deepfakes, highly realistic deepfakes are able to overcome this analytical scrutiny. Our results suggest that deepfake financial news poses novel threats to investors.

Risk occupies a central role in both the theory and practice of decision-making. Although it is deeply implicated in many conditions involving dysfunctional behavior and thought, modern theoretical approaches to understanding and mitigating risk in either one-shot or sequential settings, which are derived largely from finance and economics, have yet to permeate fully the fields of neural reinforcement learning and computational psychiatry. I will discuss the use of dynamic and static versions of one prominent approach, namely conditional value-at-risk, to examine both the nature of risk avoidant choices, encompassing such things as justified gambler's fallacies, and the optimal planning that can lead to consideration of such choices, with implications for offline, ruminative, thinking.

Nassim Nicholas Taleb develops two approaches to reasoning about extreme risk. In the first part, on the law of large numbers in the real world, he defines fat-tailed distributions and examines why conventional statistical procedures can perform poorly for economic variables. He stresses the much larger data requirements and the choice of estimators when rare observations dominate outcomes. The second part considers how to detect fragility in portfolios. Taleb describes fragility as sensitivity to volatility and argues for analysing the shape of an exposure and its response to shocks when reliable numerical estimates of tail probabilities are unavailable. The lecture connects limitations of statistical estimation with practical heuristics for identifying vulnerability.