Decision-making Overview

Principles

Definition

Decision-making is a fundamental concept and interdisciplinary field of study concerned with the cognitive and behavioral processes through which individuals, groups, or systems select a course of action from available alternatives. It investigates the identification of options, evaluation of potential outcomes, and factors influencing choice under various conditions, serving as a critical analytical lens across psychology, economics, management, and other domains.

Ontological type

Theoretical Frameworks

Cognitive Processes

Experimental Methods

Key developments

Key Figures

Bounded Rationality & Framing era

Daniel Kahneman [1], with appointments at Stanford University [3] and the University of California, Berkeley [4], helped shape bounded rationality and framing in this era. Kahneman [1] identified framing effects and value construction that deviate from expected-utility models, as detailed in Choices, values, and frames [6]. Amos Tversky [2], with affiliations at Stanford University [3] and Princeton University [5], helped advance bounded rationality and framing through collaborative work. Tversky [2] contributed to the development of framing and heuristic-based accounts that challenged traditional utility theory, as illustrated in Choices, values, and frames [6].

Dual-Process & Neural Valuation era

Antonio Rangel[1] emerged as a central figure in this era, with affiliations at Harvard University[3] and Stanford University[4]. His work identified how the vmPFC valuation system is modulated during self-control in decision-making, as described in Self-Control in Decision-Making Involves Modulation of the vmPFC Valuation System[6], and showed that value computations in charitable decision making incorporate input from regions involved in social cognition[7], while appetitive and aversive goal values are encoded in the medial orbitofrontal cortex at the time of decision making[8], thereby providing a neural mechanism for how fast valuation signals interact with slower processing in this era. Colin F. Camerer[2] is a key contributor to this period, associated with Stanford University[4] and Baylor College of Medicine[5]. His contributions include shaping formal models of self-control and decision-making that connect rapid valuation and deliberative processes, as reflected in Self-Control in Decision-Making Involves Modulation of the vmPFC Valuation System[6] and Value Computations in Ventral Medial Prefrontal Cortex during Charitable Decision Making Incorporate Input from Regions Involved in Social Cognition[7], marking a turning point in how neural valuation signals guide strategy selection in this era.

Human-in-the-Loop Decision Systems era

Yogesh K. Dwivedi [1] has affiliations with Delft University of Technology [3] and Durham University [4] during this era, and his 2019 paper Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda [7] helped frame AI-enabled decision making in complex data contexts. This paper [7] identifies transparency, governance, and user trust as core challenges for human-in-the-loop decision supports, guiding the design and policy uptake essential to the era. Min Kyung Lee [2] is affiliated with Carnegie Mellon University [5] and the University of Illinois Urbana-Champaign [6] during this era, and her 2018 paper Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management [8] investigates perceptions of algorithmic decisions and emotional responses. These findings illuminate why fairness, trust, and emotion matter for the adoption and governance of human-in-the-loop decision aids, informing design choices that preserve user oversight and acceptance.