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

Physical reasoning in complex scenes is sensitive to mass

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 57-58).Many human activities require precise judgments about the dynamics and physical properties - for example, mass - of multiple objects. Classic work suggests that people's intuitive models of physics in mass-sensitive situations are relatively poor and error-prone, based on highly simplified heuristics that apply only in special cases. These conclusions seem at odds with the breadth and sophistication of naive physical reasoning in real-world situations. Our work measures the boundaries of people's physical reasoning in mass-sensitive scenarios and tests the richness of intuitive physics knowledge in more complex scenes. We asked participants to make quantitative judgments about stability and other physical properties of virtual 3D towers composed of heavy and light blocks. We found their judgments correlated highly with a model observer that uses simulations based on realistic physical dynamics and sampling-based approximate probabilistic inference to efficiently and accurately estimate these properties. Several alternative heuristic accounts provide substantially worse fits. In a separate task, participants observed virtual 3D billiards-like movies and judged which balls were lighter. In contrast to the previous experiments, we found their judgments to be more consistent with simple, visual heuristics than a simulation-based model that updates its beliefs about mass in response to prediction errors. We conclude that rich internal physics models are likely to play a key role in guiding human common-sense reasoning in prediction-based tasks and emphasize the need for further investigation in inference-based tasks.by Jessica B. Hamrick.M.Eng.and S.B

Dagstuhl News January - December 2000

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

On the role of deduction in reasoning from uncertain premises

The probabilistic approach to reasoning hypothesizes that most reasoning, both in everyday life and in science, takes place in contexts of uncertainty. The central deductive concepts of classical logic, consistency and validity, can be generalised to cover uncertain degrees of belief. Binary consistency can be generalised to coherence, where the probability judgments for two statements are coherent if and only if they respect the axioms of probability theory. Binary validity can be generalised to probabilistic validity (p-validity), where an inference is p-valid if and only if the uncertainty of its conclusion cannot be coherently greater than the sum of the uncertainties of its premises. But the fact that this generalisation is possible in formal logic does not imply that people will use deduction in a probabilistic way. The role of deduction in reasoning from uncertain premises was investigated across ten experiments and 23 inferences of differing complexity. The results provide evidence that coherence and p-validity are not just abstract formalisms, but that people follow the normative constraints set by them in their reasoning. It made no qualitative difference whether the premises were certain or uncertain, but certainty could be interpreted as the endpoint of a common scale for degrees of belief. The findings are evidence for the descriptive adequacy of coherence and p-validity as computational level principles for reasoning. They have implications for the interpretation of past findings on the roles of deduction and degrees of belief. And they offer a perspective for generating new research hypotheses in the interface between deductive and inductive reasoning. Keywords: Reasoning; deduction; probabilistic approach; coherence; p-validit

Formal Methods Specification and Analysis Guidebook for the Verification of Software and Computer Systems

This guidebook, the second of a two-volume series, is intended to facilitate the transfer of formal methods to the avionics and aerospace community. The 1st volume concentrates on administrative and planning issues [NASA-95a], and the second volume focuses on the technical issues involved in applying formal methods to avionics and aerospace software systems. Hereafter, the term "guidebook" refers exclusively to the second volume of the series. The title of this second volume, A Practitioner's Companion, conveys its intent. The guidebook is written primarily for the nonexpert and requires little or no prior experience with formal methods techniques and tools. However, it does attempt to distill some of the more subtle ingredients in the productive application of formal methods. To the extent that it succeeds, those conversant with formal methods will also nd the guidebook useful. The discussion is illustrated through the development of a realistic example, relevant fragments of which appear in each chapter. The guidebook focuses primarily on the use of formal methods for analysis of requirements and high-level design, the stages at which formal methods have been most productively applied. Although much of the discussion applies to low-level design and implementation, the guidebook does not discuss issues involved in the later life cycle application of formal methods

On the role of deduction in reasoning from uncertain premises

The probabilistic approach to reasoning hypothesizes that most reasoning, both in everyday life and in science, takes place in contexts of uncertainty. The central deductive concepts of classical logic, consistency and validity, can be generalised to cover uncertain degrees of belief. Binary consistency can be generalised to coherence, where the probability judgments for two statements are coherent if and only if they respect the axioms of probability theory. Binary validity can be generalised to probabilistic validity (p-validity), where an inference is p-valid if and only if the uncertainty of its conclusion cannot be coherently greater than the sum of the uncertainties of its premises. But the fact that this generalisation is possible in formal logic does not imply that people will use deduction in a probabilistic way. The role of deduction in reasoning from uncertain premises was investigated across ten experiments and 23 inferences of differing complexity. The results provide evidence that coherence and p-validity are not just abstract formalisms, but that people follow the normative constraints set by them in their reasoning. It made no qualitative difference whether the premises were certain or uncertain, but certainty could be interpreted as the endpoint of a common scale for degrees of belief. The findings are evidence for the descriptive adequacy of coherence and p-validity as computational level principles for reasoning. They have implications for the interpretation of past findings on the roles of deduction and degrees of belief. And they offer a perspective for generating new research hypotheses in the interface between deductive and inductive reasoning. Keywords: Reasoning; deduction; probabilistic approach; coherence; p-validit

Semantic networks

AbstractA semantic network is a graph of the structure of meaning. This article introduces semantic network systems and their importance in Artificial Intelligence, followed by I. the early background; II. a summary of the basic ideas and issues including link types, frame systems, case relations, link valence, abstraction, inheritance hierarchies and logic extensions; and III. a survey of ‘world-structuring’ systems including ontologies, causal link models, continuous models, relevance, formal dictionaries, semantic primitives and intersecting inference hierarchies. Speed and practical implementation are briefly discussed. The conclusion argues for a synthesis of relational graph theory, graph-grammar theory and order theory based on semantic primitives and multiple intersecting inference hierarchies