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Cognitive biases, heuristics and decision-making in design for behaviour change
Copyright @ 2012 Social Science Electronic PublishingMuch human behaviour can be seen as decision-making, and so understanding and influencing those decision-making processes could be an important component in design for behaviour change. This paper examines the 'heuristics and biases' approach to modelling decision-making, and attempts to extract insights which are relevant to designers working to influence user behaviour for social or environmental benefit -- either by exploiting biases, or helping to counter those which lead to undesirable behaviour. Areas covered include a number of specific cognitive biases in detail, and the alternative perspective of Gigerenzer and others, who contend (following Herbert Simon) that many heuristics potentially leading to biases are actually ecologically rational, and part of humans' adaptive responses to situations. The design relevance of this is briefly considered, and implications for designers are summarised
Modelling default and likelihood reasoning as probabilistic
A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. 'Likely' and 'by default' are in fact treated as duals in the same sense as 'possibility' and 'necessity'. To model these four forms probabilistically, a logic QDP and its quantitative counterpart DP are derived that allow qualitative and corresponding quantitative reasoning. Consistency and consequence results for subsets of the logics are given that require at most a quadratic number of satisfiability tests in the underlying propositional logic. The quantitative logic shows how to track the propagation error inherent in these reasoning forms. The methodology and sound framework of the system highlights their approximate nature, the dualities, and the need for complementary reasoning about relevance
Coping with the Limitations of Rational Inference in the Framework of Possibility Theory
Possibility theory offers a framework where both Lehmann's "preferential
inference" and the more productive (but less cautious) "rational closure
inference" can be represented. However, there are situations where the second
inference does not provide expected results either because it cannot produce
them, or even provide counter-intuitive conclusions. This state of facts is not
due to the principle of selecting a unique ordering of interpretations (which
can be encoded by one possibility distribution), but rather to the absence of
constraints expressing pieces of knowledge we have implicitly in mind. It is
advocated in this paper that constraints induced by independence information
can help finding the right ordering of interpretations. In particular,
independence constraints can be systematically assumed with respect to formulas
composed of literals which do not appear in the conditional knowledge base, or
for default rules with respect to situations which are "normal" according to
the other default rules in the base. The notion of independence which is used
can be easily expressed in the qualitative setting of possibility theory.
Moreover, when a counter-intuitive plausible conclusion of a set of defaults,
is in its rational closure, but not in its preferential closure, it is always
possible to repair the set of defaults so as to produce the desired conclusion.Comment: Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996
Independence - revision and defaults
We investigate different aspects of independence here, in the context of
theory revision, generalizing slightly work by Chopra, Parikh, and Rodrigues,
and in the context of preferential reasoning
KR: An Architecture for Knowledge Representation and Reasoning in Robotics
This paper describes an architecture that combines the complementary
strengths of declarative programming and probabilistic graphical models to
enable robots to represent, reason with, and learn from, qualitative and
quantitative descriptions of uncertainty and knowledge. An action language is
used for the low-level (LL) and high-level (HL) system descriptions in the
architecture, and the definition of recorded histories in the HL is expanded to
allow prioritized defaults. For any given goal, tentative plans created in the
HL using default knowledge and commonsense reasoning are implemented in the LL
using probabilistic algorithms, with the corresponding observations used to
update the HL history. Tight coupling between the two levels enables automatic
selection of relevant variables and generation of suitable action policies in
the LL for each HL action, and supports reasoning with violation of defaults,
noisy observations and unreliable actions in large and complex domains. The
architecture is evaluated in simulation and on physical robots transporting
objects in indoor domains; the benefit on robots is a reduction in task
execution time of 39% compared with a purely probabilistic, but still
hierarchical, approach.Comment: The paper appears in the Proceedings of the 15th International
Workshop on Non-Monotonic Reasoning (NMR 2014
A reconstruction of the multipreference closure
The paper describes a preferential approach for dealing with exceptions in
KLM preferential logics, based on the rational closure. It is well known that
the rational closure does not allow an independent handling of the inheritance
of different defeasible properties of concepts. Several solutions have been
proposed to face this problem and the lexicographic closure is the most notable
one. In this work, we consider an alternative closure construction, called the
Multi Preference closure (MP-closure), that has been first considered for
reasoning with exceptions in DLs. Here, we reconstruct the notion of MP-closure
in the propositional case and we show that it is a natural variant of Lehmann's
lexicographic closure. Abandoning Maximal Entropy (an alternative route already
considered but not explored by Lehmann) leads to a construction which exploits
a different lexicographic ordering w.r.t. the lexicographic closure, and
determines a preferential consequence relation rather than a rational
consequence relation. We show that, building on the MP-closure semantics,
rationality can be recovered, at least from the semantic point of view,
resulting in a rational consequence relation which is stronger than the
rational closure, but incomparable with the lexicographic closure. We also show
that the MP-closure is stronger than the Relevant Closure.Comment: 57 page
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