@inproceedings{ito2011contextually,
author = {Jonathan Y. Ito and Stacy C. Marsella},
title = {Contextually-Based Utility: An Appraisal-Based Approach at Modeling Framing and Decisions},
booktitle = {Proceedings of the Twenty-Fifth {AAAI} Conference on Artificial Intelligence, August 7-11, 2011, San Francisco, CA, United States},
publisher = {AAAI Press},
url = {ito2011contextually.pdf},
year = {2011},
abstract = {Creating accurate computational models of human decision making is a vital step towards the realization of socially intelligent systems capable of both predicting and simulating human behavior. In modeling human decision making, a key factor is the psychological phenomenon known as ``framing'', in which the preferences of a decision maker change in response to contextual changes in decision problems. Existing approaches treat framing as a one-dimensional contextual influence based on the perception of outcomes as either gains or losses. However, empirical studies have shown that framing effects are much more multifaceted than one-dimensional views of framing suggest. To address this limitation, we propose an integrative approach to modeling framing which combines the psychological principles of cognitive appraisal theories and decision-theoretic notions of utility and probability. We show that this approach allows for both the identification and computation of the salient contextual factors in a decision as well as modeling how they ultimately affect the decision process. Furthermore, we show that our multi-dimensional, appraisal-based approach can account for framing effects identified in the empirical literature which cannot be addressed by one-dimensional theories, thereby promising more accurate models of human behavior.},
note = {to appear}
}
@inproceedings{ito2010wishful,
author = {Jonathan Y. Ito and David V. Pynadath and Liz Sonenberg and Stacy
C. Marsella},
title = {Wishful Thinking in Effective Decision Making (Extended Abstract)},
booktitle = {Proceedings of the 9th International Conference on Autonomous Agents
and Multiagent Systems (AAMAS 2010).},
pages = {1527--1528},
year = {2010},
abstract = {Creating agents that act reasonably in uncertain environments is a
primary goal of agent-based research. In this work we explore the
theory that wishful thinking can be an effective strategy in uncertain
and competitive decision scenarios. Specifically, we present the
constraints necessary for wishful thinking to outperform Expected
Utility Maximization and take instances of popular games from Game-Theoretic
literature showing how they relate to our constraints and whether
they can benefit from wishful-thinking.},
url = {ito2010wishful.pdf},
journal = {Autonomous Agents and Multi-Agent Systems},
owner = {jito}
}
@article{ito2010modeling,
author = {Ito, Jonathan and Pynadath, David and Marsella, Stacy},
title = {Modeling self-deception within a decision-theoretic framework},
journal = {Autonomous Agents and Multi-Agent Systems},
year = {2010},
volume = {20},
pages = {3--13},
number = {1},
month = {Jan},
abstract = {Computational modeling of human belief maintenance and decision-making
processes has become increasingly important for a wide range of applications.
In this paper, we present a framework for modeling the human capacity
for self-deception from a decision-theoretic perspective in which
we describe an integrated process of wishful thinking which includes
the determination of a desired belief state, the biasing of internal
beliefs towards or away from this desired belief state, and the final
decision-making process. Finally, we show that in certain situations
self-deception can be beneficial.},
day = {01},
doi = {10.1007/s10458-009-9096-7},
file = {ito2010modeling.pdf:ito2010modeling.pdf:PDF},
timestamp = {2010.03.30},
url = {ito2010modeling.pdf}
}
@inproceedings{ItoPM09selfdeceptive,
author = {Jonathan Y. Ito and David V. Pynadath and Stacy C. Marsella},
title = {Self-Deceptive Decision Making: Normative and Descriptive Insights},
booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent
Systems {AAMAS}},
year = {2009},
editor = {Carles Sierra and Cristiano Castelfranchi and Keith S. Decker and
Jaime Sim{\~a}o Sichman},
volume = {2},
pages = {1113-1120},
month = {May},
publisher = {IFAAMAS},
abstract = {Computational modeling of human belief maintenance and decision-making
processes has become increasingly important for a wide range of applications.
We present a framework for modeling the psychological phenomenon
of self-deception in a decision-theoretic framework. Specifically,
we model the self-deceptive behavior of wishful thinking as a psychological
bias towards the belief in a particularly desirable situation or
state. By leveraging the structures and axioms of expected utility
(EU) we are able to operationalize both the determination and the
application of the desired belief state with respect to the decision-making
process of expected utility maximization. While we categorize our
framework as a descriptive model of human decision making, we show
that when specific errors are present, the realized expected utility
of an action biased by wishful thinking can exceed that of an action
motivated purely by the maximization of expected utility. Finally,
in order to provide a descriptive characterization of our framework,
we present a discussion of wishful thinking with respect to the Certainty
Effect and the Allais Paradox, two specific documented inconsistencies
of human behavior. In this discussion we show that our framework
has the descriptive flexibility needed to account for both the Certainty
Effect and Allais Paradoxes.},
category = {Emotion Modeling},
url = {http://ict.usc.edu/~ito/ItoPM09selfdeceptive.pdf}
}
@inproceedings{ItoPM07decision,
author = {Jonathan Y. Ito and David V. Pynadath and Stacy C. Marsella},
title = {A Decision-Theoretic Approach to Evaluating Posterior Probabilities
of Mental Models},
booktitle = {Proceedings of the {AAAI} Workshop on Plan, Activity, and Intent
Recognition ({PAIR}-07) },
year = {2007},
editor = {Christopher Geib and David Pynadath},
volume = {WS-07-09},
series = {AAAI Technical Report},
pages = {60-65},
month = {July},
publisher = {AAAI Press},
abstract = {Agents face the problem of maintaining and updating their beliefs
over the possible mental models (whether goals, plans, activities,
intentions, etc.) of other agents in many multiagent domains. Decision-theoretic
agents typically model their uncertainty in these beliefs as a probability
distribution over their possible mental models of others. They then
update their beliefs by computing a posterior probability over mental
models conditioned on their observations. We present a novel algorithm
for performing this belief update over mental models that are in
the form of Partially Observable Markov Decision Problems (POMDPs).
POMDPs form a common model for decision-theoretic agents, but there
is no existing method for translating a POMDP, which generates deterministic
behavior, into a probability distribution over actions that is appropriate
for abductive reasoning. In this work, we explore alternate methods
to generate a more suitable probability distribution. We use a sample
multiagent scenario to demonstrate the different behaviors of the
approaches and to draw some conclusions about the conditions under
which each is successful.},
category = {Emotion Modeling},
url = {http://ict.usc.edu/~ito/ItoPM07decision.pdf}
}
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