David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Synthese 189 (S1):81-93 (2012)
Creating agents that proficiently interact with people is critical for many applications. Towards creating these agents, models are needed that effectively predict people's decisions in a variety of problems. To date, two approaches have been suggested to generally describe people's decision behavior. One approach creates a-priori predictions about people's behavior, either based on theoretical rational behavior or based on psychological models, including bounded rationality. A second type of approach focuses on creating models based exclusively on observations of people's behavior. At the forefront of these types of methods are various machine learning algorithms. This paper explores how these two approaches can be compared and combined in different types of domains. In relatively simple domains, both psychological models and machine learning yield clear prediction models with nearly identical results. In more complex domains, the exact action predicted by psychological models is not even clear, and machine learning models are even less accurate. Nonetheless, we present a novel approach of creating hybrid methods that incorporate features from psychological models in conjunction with machine learning in order to create significantly improved models for predicting people's decisions. To demonstrate these claims, we present an overview of previous and new results, taken from representative domains ranging from a relatively simple optimization problem and complex domains such as negotiation and coordination without communication.
|Keywords||Prediction models Psychological models for people’s decisions Mixed agent—human systems|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Thomas Schelling (1960). The Strategy of Conflict. Harvard University Press.
Citations of this work BETA
No citations found.
Similar books and articles
Martin Možina, Jure Žabkar, Trevor Bench-Capon & Ivan Bratko (2005). Argument Based Machine Learning Applied to Law. Artificial Intelligence and Law 13 (1):53-73.
Bryan R. Gibson, Timothy T. Rogers & Xiaojin Zhu (2013). Human Semi-Supervised Learning. Topics in Cognitive Science 5 (1):132-172.
Heidi L. Maibom (2007). Social Systems. Philosophical Psychology 20 (5):557 – 578.
Haidi L. Maibom (2007). Social Systems. Philosophical Psychology 20 (5):1-22.
Michael Ramscar, Daniel Yarlett, Melody Dye, Katie Denny & Kirsten Thorpe (2010). The Effects of Feature-Label-Order and Their Implications for Symbolic Learning. Cognitive Science 34 (6):909-957.
Colin F. Camerer (2003). Behavioral Game Theory: Plausible Formal Models That Predict Accurately. Behavioral and Brain Sciences 26 (2):157-158.
Axel Cleeremans (1993). Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing. MIT Press.
Kuo-Chin Chang, Tzung-Pei Hong & Shian-Shyong Tseng (1996). Machine Learning by Imitating Human Learning. Minds and Machines 6 (2):203-228.
Stephen Haller (2000). A Prudential Argument for Precaution Under Uncertainty and High Risk. Ethics and the Environment 5 (2):175-189.
Stephen Grossberg (1997). Neural Models of Development and Learning. Behavioral and Brain Sciences 20 (4):566-566.
Robert M. French (2002). Implicit Learning and Consciousness: An Empirical, Philosophical, and Computational Consensus in the Making. Psychology Press.
Bruce Glymour (2013). The Wrong Equations: A Reply to Gildenhuys. Biology and Philosophy 28 (4):675-681.
S. Russell (1991). Inductive Learning by Machines. Philosophical Studies 64 (October):37-64.
Jon Williamson (2004). A Dynamic Interaction Between Machine Learning and the Philosophy of Science. Minds and Machines 14 (4):539-549.
Added to index2012-10-04
Total downloads7 ( #291,760 of 1,725,238 )
Recent downloads (6 months)5 ( #134,514 of 1,725,238 )
How can I increase my downloads?