21 research outputs found
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Adaptive Economics: A neuroethological approach to the study of preferences, biases, and choice
A neuron's curse is that at every given time, with the information available to it, it must choose to either send a signal to its neighbouring cells or remain silent. It has evolved to be the optimal decision unit and, together with around 86 billion of its neighbours, the neuron keeps us alive, helps us cooperate, and allows us to successfully compete with others when resources get scarce. Yet, we, being collections of these neurons, still struggle to describe how these individual decision-makers support the broader process that is human decision-making.
Traditionally, decision theory has sought to understand human choices by relying more on mathematics than biology. This has led to the general assumption that decision-makers behave ‘as-if’ guided by mathematical rules and algorithms that are mostly static over time. In reality, however, decision-making relies on a brain that, due to its limited capacity, has evolved the ability for flexible and dynamic cognition.
The experiments presented in this thesis, build on dichotomies in human behaviour that cannot be explained by traditional economic models - first replicating these findings in rhesus macaques, then addressing the neurobiological algorithms that could reconcile these dichotomies. Specifically, I looked at the effects of different reward ranges, different levels of risk, and different experimental paradigms in shaping the way monkeys made choices. I demonstrate that, far from having the stable and fixed preferences prescribed by economic models, rhesus macaques appear to flexibly adapt their choice preferences in a way that optimizes their decision-making given their experience with the task at hand. I then elaborate on the neurobiological basis for preference adaptation, and show how incorporating simple, dynamic algorithms into economic choice models improves their predictive power.
Taken together, my results demonstrate the need for, and advantage of, integrating neuroethological thought into the current framework of decision theory.This work was made possible by funding from the European Research Council and the Wellcome Trust
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Applications of robust optimal control to decision making in the presence of uncertainty
This thesis is concerned with robustness of decision making in financial economics. Feedback control models developed in engineering are applied to three separate though linked problems in order to examine the role and impact of robustness in the creation and application of decision rules. Three problems are examined using robust optimal control techniques to evaluate the impact of robustness and stability in financial economic models. The first problem examines the use of linear models of robust optimal control in the pricing of castastrophe based derivatives and finds its relative performance to be superior to the popular jump diffusion and stochastic volatility models in the pricing of these emerging instruments. The novelty of the approach arises from the examination of the impact of robustness and stability of the pricing solution. The second problem involves robustness and stability of hedging. An alternative method of creating hedging rules is developed. The method is based on robust control Lyapunov functions that are simple, robust and stable in operation, yet in practice are not so conservative that they eliminate all trading gains. The third problem involves the development of robust control policies for managing risk, using non-linear robust optimal control techniques to provide clear evidence of superior performance of robust models when compared with existing VAR and EVT approaches to risk management. The novelty in the approach arises from the development of a simple and powerful risk management metric
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium
When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available