Impact of feedback loops on decision-making

This thesis examines the impact of feedback loops on individual decision-making. This represents a long standing interest of cognitive psychology in how well human beings are able to use external information in individual and group settings to revise their beliefs to control complex systems. This thesis consists of six chapters. Each chapter contains a literature review section, followed by empirical research used to compare theoretical frameworks to actual human performance on a range of tasks. Chapter 1 serves as an introductory chapter by placing the subsequent analysis in the multidisciplinary domain of judgement and decision-making. Chapter 2 represents the first part of the thesis and explores human performance in controlling dynamic physical simulations. It begins by revisiting Berry and Broadbent (1984) research, followed by the exploration of how well humans are able to control dynamic physical systems. The chapter is primarily concerned with exploring the limitations of human control and factors that influence it, ending with the performance comparison between human and generic reinforcement learning algorithms. Chapter 3 extends decision-making into the social domain. It explores the impact of group dynamics on individual belief revision and proposes new models that may better reflect actual belief revision. Chapter 4 looks at the impact of incentivisation on revision and accuracy. It is found that incentivisation has a minor impact on belief revision. Chapter 5 extends group decision-making into the novel domain of rank revision. This chapter seeks to better understand how humans aggregate ranks and revise their beliefs. Finally, Chapter 6 summaries the findings and draws on the research presented in this thesis to provide concluding remarks on human cognitive decision-making processes in dynamic settings

Impact of feedback loops on decision-making

This thesis examines the impact of feedback loops on individual decision-making. This represents a long standing interest of cognitive psychology in how well human beings are able to use external information in individual and group settings to revise their beliefs to control complex systems. This thesis consists of six chapters. Each chapter contains a literature review section, followed by empirical research used to compare theoretical frameworks to actual human performance on a range of tasks. Chapter 1 serves as an introductory chapter by placing the subsequent analysis in the multidisciplinary domain of judgement and decision-making. Chapter 2 represents the first part of the thesis and explores human performance in controlling dynamic physical simulations. It begins by revisiting Berry and Broadbent (1984) research, followed by the exploration of how well humans are able to control dynamic physical systems. The chapter is primarily concerned with exploring the limitations of human control and factors that influence it, ending with the performance comparison between human and generic reinforcement learning algorithms. Chapter 3 extends decision-making into the social domain. It explores the impact of group dynamics on individual belief revision and proposes new models that may better reflect actual belief revision. Chapter 4 looks at the impact of incentivisation on revision and accuracy. It is found that incentivisation has a minor impact on belief revision. Chapter 5 extends group decision-making into the novel domain of rank revision. This chapter seeks to better understand how humans aggregate ranks and revise their beliefs. Finally, Chapter 6 summaries the findings and draws on the research presented in this thesis to provide concluding remarks on human cognitive decision-making processes in dynamic settings

Rank aggregation and belief revision dynamics

In this paper, we compare several popular rank aggregation methods by the accuracy of finding the true (correct) ranked list. Our research reveals that under most common circumstances simple methods such as the average or majority actually tend to outperform computationally-intensive distance-based methods. We then conduct a study to compare how actual people aggregate ranks in a group setting. Our finding is that individuals tend to adopt the group mean in a third of all revisions, making it the most popular strategy for belief revision