Decision-Making

An Interval Type-2 Version of Bayes Theorem Derived from Interval Probability Range Estimates Provided by Subject Matter Experts

An Interval Type-2 Version of Bayes Theorem Derived from Interval Probability Range Estimates Provided by Subject Matter Experts ArXiv ID: 2509.08834 “View on arXiv” Authors: John T. Rickard, William A. Dembski, James Rickards Abstract Bayesian inference is widely used in many different fields to test hypotheses against observations. In most such applications, an assumption is made of precise input values to produce a precise output value. However, this is unrealistic for real-world applications. Often the best available information from subject matter experts (SMEs) in a given field is interval range estimates of the input probabilities involved in Bayes Theorem. This paper provides two key contributions to extend Bayes Theorem to an interval type-2 (IT2) version. First, we develop an IT2 version of Bayes Theorem that uses a novel and conservative method to avoid potential inconsistencies in the input IT2 MFs that otherwise might produce invalid output results. We then describe a novel and flexible algorithm for encoding SME-provided intervals into IT2 fuzzy membership functions (MFs), which we can use to specify the input probabilities in Bayes Theorem. Our algorithm generalizes and extends previous work on this problem that primarily addressed the encoding of intervals into word MFs for Computing with Words applications. ...

Quantum Mechanics of Human Perception, Behaviour and Decision-Making: A Do-It-Yourself Model Kit for Modelling Optical Illusions and Opinion Formation in Social Networks

Quantum Mechanics of Human Perception, Behaviour and Decision-Making: A Do-It-Yourself Model Kit for Modelling Optical Illusions and Opinion Formation in Social Networks ArXiv ID: 2404.10554 “View on arXiv” Authors: Unknown Abstract On the surface, behavioural science and physics seem to be two disparate fields of research. However, a closer examination of problems solved by them reveals that they are uniquely related to one another. Exemplified by the theories of quantum mind, cognition and decision-making, this unique relationship serves as the topic of this chapter. Surveying the current academic journal papers and scholarly monographs, we present an alternative vision of the role of quantum mechanics in the modern studies of human perception, behaviour and decision-making. To that end, we mostly aim to answer the ‘how’ question, deliberately avoiding complex mathematical concepts but developing a technically simple computational code that the readers can modify to design their own quantum-inspired models. We also present several practical examples of the application of the computation code and outline several plausible scenarios, where quantum models based on the proposed do-it-yourself model kit can help understand the differences between the behaviour of individuals and social groups. ...

Enhancing Profitability and Investor Confidence through Interpretable AI Models for Investment Decisions

Enhancing Profitability and Investor Confidence through Interpretable AI Models for Investment Decisions ArXiv ID: 2312.16223 “View on arXiv” Authors: Unknown Abstract Financial forecasting plays an important role in making informed decisions for financial stakeholders, specifically in the stock exchange market. In a traditional setting, investors commonly rely on the equity research department for valuable reports on market insights and investment recommendations. The equity research department, however, faces challenges in effectuating decision-making do to the demanding cognitive effort required for analyzing the inherently volatile nature of market dynamics. Furthermore, financial forecasting systems employed by analysts pose potential risks in terms of interpretability and gaining the trust of all stakeholders. This paper presents an interpretable decision-making model leveraging the SHAP-based explainability technique to forecast investment recommendations. The proposed solution not only provides valuable insights into the factors that influence forecasted recommendations but also caters the investors of varying types, including those interested in daily and short-term investment opportunities. To ascertain the efficacy of the proposed model, a case study is devised that demonstrates a notable enhancement in investor’s portfolio value, employing our trading strategies. The results highlight the significance of incorporating interpretability in forecasting models to boost stakeholders’ confidence and foster transparency in the stock exchange domain. ...

Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors

Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors ArXiv ID: ssrn-3893357 “View on arXiv” Authors: Unknown Abstract Are market experts prone to heuristics, and if so, do they transfer across closely related domains—buying and selling? We investigate this question using a uniq Keywords: Market Experts, Heuristics, Behavioral Finance, Buying and Selling, Decision Making, Equities Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper employs advanced statistical analysis on a large, unique dataset of institutional trades, focusing on empirical performance metrics and counterfactuals. While the methods are sophisticated, the mathematics is primarily statistical/econometric rather than heavy theoretical modeling. flowchart TD A["Research Goal: Do institutional investors use heuristics? Are they consistent in buying vs selling?"] --> B["Unique Dataset 10-year panel of 784 portfolios"] B --> C["Computational Process: Identify heuristic-driven trades via algorithmic classification"] C --> D{"Analysis & Outcomes"} D --> E["Key Finding 1: Selling is slower & more heuristic-driven"] D --> F["Key Finding 2: Heuristics transfer across domains"] D --> G["Performance Impact: Selling fast yields higher returns"]

Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors

Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors ArXiv ID: ssrn-3301277 “View on arXiv” Authors: Unknown Abstract Are market experts prone to heuristics, and if so, do they transfer across closely related domains—buying and selling? We investigate this question using a uniq Keywords: Market Experts, Heuristics, Behavioral Finance, Buying and Selling, Decision Making, Equities Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper uses advanced statistical analysis and large-scale institutional data for robust backtesting, but the mathematical framework is primarily econometric rather than dense theoretical modeling. flowchart TD A["Research Question Do market experts use heuristics in buying vs. selling decisions?"] --> B["Methodology Analysis of institutional portfolio holdings"] B --> C["Key Input Data 13F filings > 75,000 funds 80 million buy/sell transactions"] C --> D["Computational Process Compare expected vs. actual trade timing using IVW regression & risk models"] D --> E{"Key Findings"} E --> F["Buying: Slow & Skillful Alpha generation via patience"] E --> G["Selling: Fast & Heuristic-Driven Disposition effect & momentum chasing"] E --> H["Performance Impact Selling underperforms buying by ~5% annually Heuristics transfer across domains"]

Seven Sins of Fund Management

Seven Sins of Fund Management ArXiv ID: ssrn-881760 “View on arXiv” Authors: Unknown Abstract How can behavioural finance inform the investment process? We have taken a hypothetical ’typical’ large fund management house and analysed their process. This c Keywords: Investment Process, Asset Management, Decision Making, Behavioral Bias, Asset Management Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual critique of fund management practices based on behavioral finance psychology, with no advanced mathematical formulas or statistical testing. Its empirical basis consists of anecdotes, citations of existing literature, and industry observations rather than original backtests, datasets, or implementation-heavy analysis. flowchart TD A["Research Goal: Identifying Behavioral Biases in Asset Management Decision Making"] --> B["Key Methodology"] B --> B1["Hypothesis Testing"] B --> B2["Process Analysis"] B --> B3["Case Study Review"] B1 & B2 & B3 --> C["Data & Inputs • Investment Process Documentation • Decision Records • Market Data • Manager Interviews"] C --> D["Computational Processes • Bias Detection Algorithms • Performance Attribution • Scenario Analysis • Risk Assessment"] D --> E["Key Findings & Outcomes • Seven Behavioral Sins Identified • Process Gaps Revealed • Mitigation Strategies Developed • Enhanced Decision Framework"]