How much do incentives affect car purchase? Agent-based microsimulation of consumer choice of new cars—Part I: Model structure, simulation of bounded rationality, and model validation

Energy Policy ◽  
2009 ◽  
Vol 37 (3) ◽  
pp. 1072-1082 ◽  
Author(s):  
Michel G. Mueller ◽  
Peter de Haan
Keyword(s):  
Bounded Rationality ◽  
Model Validation ◽  
Consumer Choice ◽  
Model Structure ◽  
Agent Based ◽  
Structure Simulation

Agent-Based Computational Model

2017 ◽  
Author(s):  
Joshua M. Epstein
Keyword(s):  
Computational Model ◽  
Bounded Rationality ◽  
Time Scales ◽  
Spatially Explicit ◽  
Agent Based ◽  
Affective Component ◽  
Elementary Type ◽  
Social Component ◽  
Different Time Scales

This part describes the agent-based and computational model for Agent_Zero and demonstrates its capacity for generative minimalism. It first explains the replicability of the model before offering an interpretation of the model by imagining a guerilla war like Vietnam, Afghanistan, or Iraq, where events transpire on a 2-D population of contiguous yellow patches. Each patch is occupied by a single stationary indigenous agent, which has two possible states: inactive and active. The discussion then turns to Agent_Zero's affective component and an elementary type of bounded rationality, as well as its social component, with particular emphasis on disposition, action, and pseudocode. Computational parables are then presented, including a parable relating to the slaughter of innocents through dispositional contagion. This part also shows how the model can capture three spatially explicit examples in which affect and probability change on different time scales.

Agent-based mechanism design – investigating bounded rationality concepts in a budgeting context

2017 ◽  
Vol 23 (1/2) ◽  
pp. 13-27 ◽  
Author(s):  
Iris Lorscheid ◽  
Matthias Meyer
Keyword(s):  
Mechanism Design ◽  
Bounded Rationality ◽  
Human Behavior ◽  
Design Methodology ◽  
Content Type ◽  
Agent Based ◽  
Decision Context ◽  
Agent Learning ◽  
Groves Mechanism ◽  
Research Questions

Purpose This study aims to demonstrate how agent-based simulation (ABS) may provide a computational testbed for mechanism design using concepts of bounded rationality (BR). ABS can be used to systematically derive and formalize different models of BR. This allows us to identify the cognitive preconditions for behavior intended by the mechanism and thereby to derive implications for the design of mechanisms. Design/methodology/approach Based on an analysis of the requirements of the decision context, the authors describe a systematic way of incorporating different BR concepts into an agent learning model. The approach is illustrated by analyzing an incentive scheme suggested for truthful reporting in budgeting contexts, which is an adapted Groves mechanism scheme. Findings The study describes systematic ways in which to derive BR agents for research questions where behavioral aspects might matter. The authors show that BR concepts may lead to other outcomes than the intended truth-inducing effect. A modification of the mechanism to more distinguishable levels of payments improves the results in terms of the intended effect. Research limitations/implications The presented BR concepts as simulated by agent models cannot model human behavior in its full complexity. The simplification of complex human behavior is a useful analytical construct for the controlled analysis of a few aspects and an understanding of the potential consequences of those aspects of human behavior for mechanism design. Originality/value The paper specifies the idea of a computational testbed for mechanism design based on BR concepts. Beyond this, a systematic and stepwise approach is shown to formalize bounded rational behavior by agents based on a requirements analysis, including benchmark models for the comparison and evaluation of BR concepts.

The Rule of Experts

2015 ◽  
pp. 342-363
Author(s):  
Roger Koppl
Keyword(s):  
Economic Theory ◽  
Medical Research ◽  
Bounded Rationality ◽  
Division Of Labor ◽  
Choice Theory ◽  
Individual Autonomy ◽  
Agent Based ◽  
Engineering Studies ◽  
Law And Justice ◽  
Epistemic Systems

The division of labor creates a division of knowledge, which creates expertise and the problem of experts. The rule of experts exists when experts have an epistemic monopoly and choose for others. Generally, experts may have power that threatens individual autonomy. Competition tends to dissipate the power of experts, although the details of market structure matter. Even well-meaning experts may fail because they have bounded rationality. Epistemic monopoly increases the risks of error and expert failure; competition reduces them. Information choice theory is an economic theory of experts. It may help in the design of epistemic systems, which are agent-based processes viewed from perspective of their knowledge properties. Epistemic engineering studies the design principles of epistemic systems. Economists should consider the epistemic properties of alternative institutions to minimize the problem of experts and avoid the rule of experts. Applications discussed include religion, law and justice, and medical research.

Building ABMs to Control the Emergence of Crisis Analyzing Agents' Behavior

2014 ◽  
pp. 312-335 ◽  
Author(s):  
Luca Arciero ◽  
Cristina Picillo ◽  
Sorin Solomon ◽  
Pietro Terna
Keyword(s):  
Social Behavior ◽  
Financial Crisis ◽  
Complex Systems ◽  
Bounded Rationality ◽  
Financial Institutions ◽  
Banking System ◽  
Learning Processes ◽  
Emergent Properties ◽  
Natural Phenomena ◽  
Agent Based

Agent-based models (ABMs) are quite new in the modeling landscape; they emerged on the scene in the 1990s. ABMs have a clear advantage over other approaches: they create the capacity to manage learning processes in agents and discover novelties in their behavior. In addition to bounded rationality assumptions, ABMs share a number of peculiar characteristics: first of all, a bottom-up perspective is assumed where the properties of macro-dynamics are emergent properties of micro-dynamics involving individuals as heterogeneous agents who live in complex systems that evolve through time. To apply this framework to financial crisis analysis, a simplified implementation of the SWARM protocol (www.swarm.org), based on Python, is introduced. The result is the Swarm-Like Agent Protocol in Python (SLAPP). Using SLAPP, it is possible to focus on natural phenomena and social behavior. In the case of this chapter, the authors focus on the banking system, recreating the interactions of a community of financial institutions that act in the payment system and in the interbank market for short-term liquidity.

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