A Conceptual Model of Investor Behavior

Based on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is influenced by a number of interdependent variables and driven by dual mental systems, the interplay of which contributes to boundedly rational behavior where investors use various heuristics and may exhibit behavioral biases. In the modeling tradition of cognitive science and intelligent systems, the investor is seen as a learning, adapting, and evolving entity that perceives the environment, processes information, acts upon it, and updates his or her internal states. This conceptual model can be used to build stylized representations of (classes of) individual investors, and further studied using the paradigm of agent-based artificial financial markets. By allowing us to implement individual investor behavior, to choose various market mechanisms, and to analyze the obtained asset prices, agent-based models can bridge the gap between the micro level of individual investor behavior and the macro level of aggregate market phenomena. It has been recognized, yet not fully explored, that these models could be used as a tool to generate or test various behavioral hypothesis.behavioral finance;financial decision making;agent-based artificial financial markets;cognitive modeling;investor behavior

A Conceptual Model of Investor Behavior

Based on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is influenced by a number of interdependent variables and driven by dual mental systems, the interplay of which contributes to boundedly rational behavior where investors use various heuristics and may exhibit behavioral biases. In the modeling tradition of cognitive science and intelligent systems, the investor is seen as a learning, adapting, and evolving entity that perceives the environment, processes information, acts upon it, and updates his or her internal states. This conceptual model can be used to build stylized representations of (classes of) individual investors, and further studied using the paradigm of agent-based artificial financial markets. By allowing us to implement individual investor behavior, to choose various market mechanisms, and to analyze the obtained asset prices, agent-based models can bridge the gap between the micro level of individual investor behavior and the macro level of aggregate market phenomena. It has been recognized, yet not fully explored, that these models could be used as a tool to generate or test various behavioral hypothesis

Human-AI symbiosis: The best approach for AI implementation in business decision-making in complex systems

In today's business landscape, there is significant discourse surrounding the role of Artificial Intelligence (AI) in various aspects of business operations. Decision-making, in particular, is a crucial component of every business-related activity. As businesses expanded and generated massive amounts of data, it became clear that humans alone could no longer make consistently accurate decisions. Moreover, it is demonstrated that humans often rely on heuristics and cognitive biases in their decision-making, leading to suboptimal outcomes. Given today's business environment's complexity, instability, and interconnected nature, businesses possess all the characteristics of complex systems. With the aid of AI, decision-making can be significantly enhanced. Various subfields of AI, such as artificial neural networks, fuzzy logic networks, and agents, have been developed in recent years, playing a pivotal role in enabling AI-driven decision-making. Findings through using purposeful and complex systems suggest that although AI subfields in decision-making can make sound decisions, they exhibit deficiencies in complex systems where human interaction and interconnectedness across different organizational levels are present. Currently, AI technology is not equipped to address these challenges. As a result, the decision-making process should not be entirely delegated to machines and AI. This discussion gives rise to the duality of augmentation and automation. Decision-making can be categorized into three levels: operational, tactical, and strategic, ranging from structured to unstructured decisions. The analysis reveals that AI performs admirably as an assistant or replacement tool at the operational level. However, as moving towards tactical and strategic decisions, although its augmentation abilities remain somewhat consistent, its capabilities for replacement and automation diminish significantly. Consequently, AI is believed to lack the ability to automate strategic and unstructured business decisions completely

Risk Management Psychology and Practice

This thesis examines the theory of human behavior towards risk and uncertainty in addition to the psychological effects they have on the managerial decision-making process. Analysis indicates that risk often produces a negative reaction in individuals, which ultimately ends in avoidance. I describe how our responses to risk are often influenced by heuristic biases, psychometric paradigms, and emotional literacy. These influences form the attitudes that become mental hurdles to approaching risk objectively and proactively. The collective attitudes within organizations contribute to the overall risk culture. This thesis identifies competencies required to establish a mature risk culture which is the critical foundation for implementing risk management best practices. Once the foundation is in place, there are formal methodologies to proactively identify areas of uncertainty and provide qualitative and quantitative assessments. The objective is to provide managers the proper tools to develop sound responses to risk based upon objective analysis of facts in lieu of distorted biases. A proactive approach in seeking out risk instills the confidence in managers to manage risk effectively

Egocentric Bias and Doubt in Cognitive Agents

Modeling social interactions based on individual behavior has always been an area of interest, but prior literature generally presumes rational behavior. Thus, such models may miss out on capturing the effects of biases humans are susceptible to. This work presents a method to model egocentric bias, the real-life tendency to emphasize one's own opinion heavily when presented with multiple opinions. We use a symmetric distribution, centered at an agent's own opinion, as opposed to the Bounded Confidence (BC) model used in prior work. We consider a game of iterated interactions where an agent cooperates based on its opinion about an opponent. Our model also includes the concept of domain-based self-doubt, which varies as the interaction succeeds or not. An increase in doubt makes an agent reduce its egocentricity in subsequent interactions, thus enabling the agent to learn reactively. The agent system is modeled with factions not having a single leader, to overcome some of the issues associated with leader-follower factions. We find that agents belonging to factions perform better than individual agents. We observe that an intermediate level of egocentricity helps the agent perform at its best, which concurs with conventional wisdom that neither overconfidence nor low self-esteem brings benefits

An inclusive taxonomy of behavioral biases

This paper overviews the theoretical and empirical research on behavioral biases and their influence in the literature. To provide a systematic exposition, we present a unified framework that takes the reader through an original taxonomy, based on the reviews of relevant authors in the field. In particular, we establish three broad categories that may be distinguished: heuristics and biases; choices, values and frames; and social factors. We then describe the main biases within each category, and revise the main theoretical and empirical developments, linking each bias with other biases and anomalies that are related to them, according to the literature

The effect of behavioral biases on financial decisions

Financial management decisions are made by people, and people, in all instances, are shaped by their behavioral traits. Here we provide extensive insight on the theoretical and empirical analysis made on cognitive biases and their influence on financial decisions. To provide a systematic exposition, we set three broad categories: heuristics and biases, choices (including framing and preferences) and social factors. We then describe the main biases within each category and provide an extensive revision of the main theoretical and empirical developments about their impact on financial decisions.Las decisiones de gestión financiera las toman las personas, y las personas, en todos los casos, están determinadas por sus rasgos de comportamiento. Aquí proporcionamos una visión amplia del análisis teórico y empírico realizado sobre los sesgos cognitivos y su influencia en las decisiones financieras. Para proporcionar una exposición sistemática, establecemos tres categorías amplias: heurísticas y sesgos, elecciones (incluidos encuadres y preferencias) y factores sociales. A continuación, describimos los principales sesgos dentro de cada categoría y proporcionamos una revisión exhaustiva de los principales desarrollos teóricos y empíricos sobre su impacto en las decisiones financieras

Consumer judgment and forecasting using online word-of-mouth

Empowered by information technology, modern consumers increasingly rely upon online word-of-mouth (WOM--e.g., product reviews) to guide their purchase decisions. This dissertation investigates how WOM information is processed by consumers and its downstream consequences. First, the value of specific types of word-of-mouth information (e.g., numeric ratings, text commentary, or both) was explored for making forecast. After proposing an anchoring-and-adjustment framework for the utilization of WOM to inform consumer forecasts, I support this framework with a series of experiments. Results demonstrate that the relative forecasting advantage of different information types is a function of the extent to which consumer and reviewer have similar product-level preferences ('source-receiver similarity'). Second, I investigate the process by which dispersion--the degree to which opinions are divided for a product or service--in WOM is interpreted. Using an attribution-based approach, I argue that the effect of WOM dispersion is dependent on the perceived cause of that dispersion, which is systematically related to perceptions of preference heterogeneity in a product category. For products for which preferences are expected to vary, dispersion is likely to be attributed to the reviewers rather than the product itself, and therefore tolerated. I provide evidence for my hypotheses in a series of experiments where WOM dispersion is manipulated and respondents make choices and indicate purchase intentions.PhDCommittee Chair: Bond, Samuel D.; Committee Member: Feldman, Jack M.; Committee Member: Hamilton, Ryan; Committee Member: Lurie, Nicholas H.; Committee Member: Van Ittersum, Koer