The Optimality of Decision Making During Motor Learning

In our daily lives, we often must predict how well we are going to perform in the future based on an evaluation of our current performance and an assessment of how much we will improve with practice. Such predictions can be used to decide whether to invest our time and energy in learning and, if we opt to invest, what rewards we may gain. This thesis investigated whether people are capable of tracking their own learning (i.e. current and future motor ability) and exploiting that information to make decisions related to task reward. In experiment one, participants performed a target aiming task under a visuomotor rotation such that they initially missed the target but gradually improved. After briefly practicing the task, they were asked to select rewards for hits and misses applied to subsequent performance in the task, where selecting a higher reward for hits came at a cost of receiving a lower reward for misses. We found that participants made decisions that were in the direction of optimal and therefore demonstrated knowledge of future task performance. In experiment two, participants learned a novel target aiming task in which they were rewarded for target hits. Every five trials, they could choose a target size which varied inversely with reward value. Although participants’ decisions deviated from optimal, a model suggested that they took into account both past performance, and predicted future performance, when making their decisions. Together, these experiments suggest that people are capable of tracking their own learning and using that information to make sensible decisions related to reward maximization.

A Collaborative Multiagent Framework based on Online Risk-Aware Planning and Decision-Making

Planning is an essential process in teams of multiple agents pursuing a common goal. When the effects of actions undertaken by agents are uncertain, evaluating the potential risk of such actions alongside their utility might lead to more rational decisions upon planning. This challenge has been recently tackled for single agent settings, yet domains with multiple agents that present diverse viewpoints towards risk still necessitate comprehensive decision making mechanisms that balance the utility and risk of actions. In this work, we propose a novel collaborative multi-agent planning framework that integrates (i) a team-level online planner under uncertainty that extends the classical UCT approximate algorithm, and (ii) a preference modeling and multicriteria group decision making approach that allows agents to find accepted and rational solutions for planning problems, predicated on the attitude each agent adopts towards risk. When utilised in risk-pervaded scenarios, the proposed framework can reduce the cost of reaching the common goal sought and increase effectiveness, before making collective decisions by appropriately balancing risk and utility of actions. 

Decision-making process in Portuguese Erasmus student mobility: case study

With the evolution of nowadays knowledge-based economies, the labour class becomes more competitive. As a way of getting skills that bring benefits to their careers, university students take advantage of the many opportunities available and go abroad to study. This study develops and empirically tests a structural model that examines the antecedents that influence the decision-making process of an Erasmus student under mobility for studies (EMS) in Aveiro, Coimbra and Porto (2014-2015). Reliability analysis, exploratory factor analysis and linear regressions were used to evaluate the model. Based on a survey with a sample of 872 valid responses, this study has demonstrated that EMS students are also influenced by touristic factors, which gives support to what has recently been approached by other authors. Conclusions and suggestions can be applied by other organizations, mainly Higher Education Institutions in order to attract more EMS students.

Designing Efficient Subsidy Policies in a Regional Economy: A Multicriteria Decision-Making (MCDM)-Computable General Equilibrium (CGE) Approach

Since policy-makers usually pursue several conflicting objectives, policy-making can be understood as a multicriteria decision problem. Following the methodological proposal by André and Cardenete (2005) André, F. J. and Cardenete, M. A. 2005. Multicriteria Policy Making. Defining Efficient Policies in a General Equilibrium Model, Seville: Centro de Estudios Andaluces. Working Paper No. E2005/04, multi-objective programming is used in connection with a computable general equilibrium model to represent optimal policy-making and to obtain so-called efficient policies in an application to a regional economy (Andalusia, Spain). This approach is applied to the design of subsidy policies under two different scenarios. In the first scenario, it is assumed that the government is concerned just about two objectives: ensuring the profitability of a key strategic sector and increasing overall output. Finally, the scope of the exercise is enlarged by solving a problem with seven policy objectives, including both general and sectorial objectives. It is concluded that the observed policy could have been Pareto-improved in several directions.

Identifying and enhancing risk thresholds in the detection of elder financial abuse: A signal detection analysis of professionals' decision making

Background: Financial abuse of elders is an under acknowledged problem and professionals' judgements contribute to both the prevalence of abuse and the ability to prevent and intervene. In the absence of a definitive "gold standard" for the judgement, it is desirable to try and bring novice professionals' judgemental risk thresholds to the level of competent professionals as quickly and effectively as possible. This study aimed to test if a training intervention was able to bring novices' risk thresholds for financial abuse in line with expert opinion. Methods: A signal detection analysis, within a randomised controlled trial of an educational intervention, was undertaken to examine the effect on the ability of novices to efficiently detect financial abuse. Novices (n = 154) and experts (n = 33) judged "certainty of risk" across 43 scenarios; whether a scenario constituted a case of financial abuse or not was a function of expert opinion. Novices (n = 154) were randomised to receive either an on-line educational intervention to improve financial abuse detection (n = 78) or a control group (no on-line educational intervention, n = 76). Both groups examined 28 scenarios of abuse (11 "signal" scenarios of risk and 17 "noise" scenarios of no risk). After the intervention group had received the on-line training, both groups then examined 15 further scenarios (5 "signal" and 10 "noise" scenarios). Results: Experts were more certain than the novices, pre (Mean 70.61 vs. 58.04) and post intervention (Mean 70.84 vs. 63.04); and more consistent. The intervention group (mean 64.64) were more certain of abuse post-intervention than the control group (mean 61.41, p = 0.02). Signal detection analysis of sensitivity (Á) and bias (C) revealed that this was due to the intervention shifting the novices' tendency towards saying "at risk" (C post intervention -.34) and away from their pre intervention levels of bias (C-.12). Receiver operating curves revealed more efficient judgments in the intervention group. Conclusion: An educational intervention can improve judgements of financial abuse amongst novice professionals.

Attitudes towards risk in financial decision making

Doutoramento em Gestão

Transdiscipline and research in health: Science, society and decision making

Significant advances in science should be given to addressing the needs of society and the historical context of the territories. Although technological developments that began with modernity and the industrial revolution allowed human beings to control the resources of nature to put to your service without limits, it is clear that the crisis of the prevailing development models manifest themselves in many ways but with three common denominators: environmental degradation, social injustice and extreme poverty. Consequently, today should not be possible to think a breakthrough in the development of science without addressing global environmental problems and the deep social injustices that increase at all scales under the gaze, impassively in many occasions, of formal science.

Enriching understanding of young adult voter decision-making in the Australian compulsory voting context

This thesis has provided insight into considering how the consumer behaviour theoretical concept of usage impacts on lower-involvement young adult voter decision-making within the Australian compulsory voting context. This differentiated replication of previous research extends the range of results under which decision-making factors hold in voter behaviour.

Rise of the machines: a critical consideration of automated leadership decision making in organizations

Machines are increasingly becoming a substitute for human skills and intelligence in a number of fields where decisions that are crucial to group performance have to be taken under stringent constraints—for example, when an army contingent has to devise battlefield tactics or when a medical team has to diagnose and treat a life-threatening condition or illness. We hypothesize a scenario where similar machine-based intelligent technology is available to support, and even substitute human decision making in an organizational leadership context. We do not engage in any metaphysical debate on the plausibility of such a scenario. Rather, we contend that given what we observe in several other fields of human decision making, such a scenario may very well eventuate in the near future. We argue a number of “positives” that can be expected to emerge out of automated group and organizational leadership decision making. We also posit several anti-theses—“negatives” that can also potentially emerge from the hypothesized scenario and critically consider their implications. We aim to bring leadership and organization theorists, as well as researchers in machine intelligence, together at the discussion table for the first time and postulate that while leadership decision making in a group/organizational context could be effectively delegated to an artificial-intelligence (AI)-based decision system, this would need to be subject to the devising of crucial safeguarding conditions.

Enabling effective operational decision making on a Combined Heat and Power System using the 5C architecture

The use of Cyber Physical Systems (CPS) to optimise industrial energy systems is an approach which has the potential to positively impact on manufacturing sector energy efficiency. The need to obtain data to facilitate the implementation of a CPS in an industrial energy system is however a complex task which is often implemented in a non-standardised way. The use of the 5C CPS architecture has the potential to standardise this approach. This paper describes a case study where data from a Combined Heat and Power (CHP) system located in a large manufacturing company was fused with grid electricity and gas models as well as a maintenance cost model using the 5C architecture with a view to making effective decisions on its cost efficient operation. A control change implemented based on the cognitive analysis enabled via the 5C architecture implementation has resulted in energy cost savings of over €7400 over a four-month period, with energy cost savings of over €150,000 projected once the 5C architecture is extended into the production environment.

The right to supported decision-making for people rarely heard

This thesis explored supported decision-making for people with severe or profound intellectual disability. The findings provide guidance to supporters and policy makers to assist people with severe or profound intellectual disability to lead maximally autonomous lives, a clear obligation of Australia under the Convention on the Rights of Persons with Disabilities.