Base rate neglect for the wealth of populations

Base rate neglect has been shown to be a very robust bias in human information processing. It has also been show to be ecologically rational in some environments. However, when arguing about base rate neglect usually isolated individuals are considered. I complement these results by showing that in many scenarios of social learning a base rate neglect increases a population's wealth. I thereby strengthen the argument that the presence of base rate neglect could be evolutionary stable. I pick up a model of social learning that has been used to demonstrate the potential benefits of overconfidence. Individuals are confronted with a safe and a risky option. They receive a private signal about the risky option's outcome, they decide in an exogenously given sequence, and they observe decisions of preceding individuals. I first deviate from the original model by incorporating base rates that differ from fifty-fifty and show that under weighting this base rate can be for the wealth of a population. Then I analyse how the optimal base rate neglect reacts to changes in payoffs. I show that for large set of settings under weighting the base rate is still positive, but for a smaller subset it decreases wealth insteadcognitive biases, base rate neglect, social learning, ecological rationality

Behavioral Social Learning

We revisit the economic models of social learning by assuming that individuals update their beliefs in a non-Bayesian way. Individuals either overweigh or underweigh (in Bayesian terms) their private information relative to the public information revealed by the decisions of others and each individual's updating rule is private information. First, we consider a setting with perfectly rational individuals with a commonly known distribution of updating rules. We show that introducing heterogeneous updating rules in a simple social learning environment reconciles equilibrium predictions with laboratory evidence. Additionally, a model of social learning with bounded private beliefs and sufficiently rich updating rules corresponds to a model of social learning with unbounded private beliefs. A straightforward implication is that heterogeneity in updating rules is efficiency-enhancing in most social learning environments. Second, we investigate the implications of heterogeneous updating rules in social learning environments where individuals only understand the relation between the aggregate distribution of decisions and the state of the world. Unlike in rational social learning, heterogeneous updating rules do not lead to a substantial improvement of the societal welfare and there is always a non-negligible likelihood that individuals become extremely and wrongly conï¬dent about the state of the world.Social learning, Non-Bayesian updating, Herding, Informational cascades

Herding in Financial Behaviour: A Behavioural and Neuroeconomic Analysis of Individual Differences

Experimental analyses have identified significant tendencies for individuals to follow herd decisions, a finding which has been explained using Bayesian principles. This paper outlines the results from a herding task designed to extend these analyses using evidence from a functional magnetic resonance imaging (fMRI) study. Empirically, we estimate logistic functions using panel estimation techniques to quantify the impact of herd decisions on individuals' financial decisions. We confirm that there are statistically significant propensities to herd and that social information about others' decisions has an impact on individuals' decisions. We extend these findings by identifying associations between herding propensities and individual characteristics including gender, age and various personality traits. In addition fMRI evidence shows that individual differences correlate strongly with activations in the amygdala – an area of the brain commonly associated with social decision-making. Individual differences also correlate strongly with amygdala activations during herding decisions. These findings are used to construct a two stage least squares model of financial herding which confirms that individual differences and neural responses play a role in modulating the propensity to herd.amygdal

Herding in Financial Behaviour

Experimental analyses have identified significant tendencies for individuals to follow herd decisions, a finding which has been explained using Bayesian principles. This paper outlines the results from a herding task designed to extend these analyses using evidence from a functional magnetic resonance imaging (fMRI) study. Empirically, we estimate logistic functions using panel estimation techniques to quantify the impact of herd decisions on individuals' financial decisions. We confirm that there are statistically significant propensities to herd and that social information about others' decisions has an impact on individuals' decisions. We extend these findings by identifying associations between herding propensities and individual characteristics including gender, age and various personality traits. In addition fMRI evidence shows that individual differences correlate strongly with activations in the amygdala ﾖ an area of the brain commonly associated with social decision-making. Individual differences also correlate strongly with amygdala activations during herding decisions. These findings are used to construct a two stage least squares model of financial herding which confirms that individual differences and neural responses play a role in modulating the propensity to herd

Impacts of Personality on Herding in Financial Decision-Making

It is well known that rational bubbles can be sustained in balanced growth path of a deterministic economy when the return to capital r is equal to the growth rate g. When there is a lack of stores of value, bubbles can implement an efficient allocation. This paper considers a world where r fluctuates over time due to shocks to the marginal productivity of capital. Then, bubbles further efficiency, though they cannot implement first best. While bubbles can only be sustained when r = g in a deterministic economy, r > g "on average" in a stochastic economy. Fiscal policy improves welfare by adding an extra asset. Where only the elderly contribute to shifting resources between investment and consumption in a bubbly economy, fiscal policy allows part of that burden to be shifted to the young. Contrary to common wisdom, trade in bubbly assets implements intergenerational transfers, while fiscal policy implements intragenerational transfers. Hence, while bubbles and fiscal policy are perfect substitutes in the deterministic economy, fiscal policy dominates bubbles in a stochastic economy. For plausible parameter values, a higher degree of dynamic inefficiency should lead to a higher sovereign debt

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

Sequencing in Intelligent Tutoring Systems based on online learning Recommenders

In dieser Arbeit entwickeln und testen wir Algorithmen für Learning Analytics, die die personalisierte Sequenzierung von Matheaufgaben erlauben. Die Sequenzierung schlägt die nächste Aufgabe einem Schüler vor, die seine Lernbedürfnisse entspricht. Unsere Lösung basiert auf Vygotskys „Zone of Proximal Development“ (ZPD), das die weder zu einfachen noch zu schwierigen Aufgaben für den Schüler bestimmt. Der Sequenzer, auch Vygotsky Policy Sequencer genannt, ist in der Lage Aufgaben im ZPD zu erkennen, dank die von einem Vorhersagealgorithmus geschätzte zukünftige Leistung des Schülers. Die Arbeit enthält folgende Beiträge: (1) Die Evaluation der Anwendbarkeit von Matrix Factorization als Inhaltsdomäne unabhängige Algorithmus für die Vorhersage der Leistung der Schüler. (2) Anpassung und Evaluation eines Matrix Factorization basierenden Algorithmus, der die zeitliche Evolution der Schülerkenntnisse einbezieht. (3) Entwicklung von zwei Ansätzen für die Aktualisierung von Matrix Factorization basierenden Modellen durch den Kalman Filter. Zwei Aktualisierungsfunktionen sind benutzt: (a) eine einfache, die nur die letzte vom Schüler gesehene Aufgabe betrachtet, und (b) eine, die in der Lage ist, seine fehlenden Kompetenzen einzuschätzen. (4) Ein neues Verfahren von Machine Learning gesteuerte Sequenzer zu testen durch die Modellierung einer simulierten Umgebung, die aus simulierte Schülern und Aufgaben mit stetigen erzielten und gebrauchten Fähigkeiten und Schwierigkeitsgraden besteht. (5) Die Entwicklung einer minimal eingreifenden API für die leichte Integration von Machine Learning basierende Komponente in größere Systeme, um das Integrationsrisiko und die Kosten vom Know-How-Transfer zu minimieren. Dank all diesen Beiträgen, wurde der VPS in ein großes kommerzielles System integriert und mit 100 Kinder für einen Monat getestet. Der VPS zeigte Lerneffekte und wahrgenommene Erlebnisse, die mit den von den ITS Sequenzer vergleichbar sind. Infolge der besseren VPS Modellierfähigkeiten konnten die Schüler beendeten die Aufgaben schneller lösen.In this thesis we design and test Learning Analytics algorithms for personalized tasks' sequencing that suggests the next task to a student according to his/her specific needs. Our solution is based on a sequencing policy derived from the Vygotsky's Zone of Proximal Development (ZPD), which denes those tasks that are neither too easy not too dicult for the student. The sequencer, called Vygotsky Policy Sequencer (VPS), can identify tasks in the ZPD thanks to the information it receives from performance prediction algorithms able to estimate the knowledge of the student. Under this context we describe hereafter the thesis contributions. (1) A feasibility evaluation of domain independent Matrix Factorization applied in ITS for Performance Prediction. (2) An adaption and the related evaluation of a domain independent update for online learning Matrix Factorization in ITS. (3) A novel Matrix Factorization update method based on Kalman Filters approach. Two different updating functions are used: (a) a simple one considering the task just seen, and (b) one able to derive the skills' deficiency of the student. (4) A new method for offline testing of machine learning controlled sequencers by modeling simulated environment composed by a simulated students and tasks with continuous knowledge and score representation and different diffculty levels. (5) The design of a minimal invasive API for the lightweight integration of machine learning components in larger systems to minimize the risk of integration and the cost of expertise transfer. Profiting from all these contributions, the VPS was integrated in a commercial system and evaluated with 100 children over a month. The VPS showed comparable learning gains and perceived experience results with those of the ITS sequencer. Finally, thanks to its better modeling abilities, the students finish faster the assigned tasks