“Know What You Know”: Predicting Behavior for Learning-Enabled Systems When Facing Uncertainty

2021 ◽  
Author(s):  
Michael Austin Langford ◽  
Betty H.C. Cheng
Keyword(s):  
Predicting Behavior

Indirect Assessment of Attitudes with Response-Time-Based Measures

2006 ◽  
Vol 37 (3) ◽  
pp. 131-139 ◽  
Author(s):  
Juliane Degner ◽  
Dirk Wentura ◽  
Klaus Rothermund
Keyword(s):  
Social Cognitive ◽  
Incremental Validity ◽  
Self Report ◽  
Small Scale ◽  
Implicit Measures ◽  
Scale Theory ◽  
Starting Point ◽  
Self Reports ◽  
Predicting Behavior ◽  
Social Cognitive Theories

Abstract: We review research on response-latency based (“implicit”) measures of attitudes by examining what hopes and intentions researchers have associated with their usage. We identified the hopes of (1) gaining better measures of interindividual differences in attitudes as compared to self-report measures (quality hope); (2) better predicting behavior, or predicting other behaviors, as compared to self-reports (incremental validity hope); (3) linking social-cognitive theories more adequately to empirical research (theory-link hope). We argue that the third hope should be the starting point for using these measures. Any attempt to improve these measures should include the search for a small-scale theory that adequately explains the basic effects found with such a measure. To date, small-scale theories for different measures are not equally well developed.

THE RESPONSE OF STRUCTURES TO FIRE -- ADVANCES AND CHALLENGES IN PREDICTING BEHAVIOR

Equipment ◽  
2006 ◽  
Author(s):  
William L. Grosshandler
Keyword(s):  
Predicting Behavior

Validation of an attitude scale as a device for predicting behavior.

1956 ◽  
Vol 40 (5) ◽  
pp. 347-349 ◽  
Author(s):  
Peter A. Holman
Keyword(s):  
Attitude Scale ◽  
Predicting Behavior

Predicting behavior on a computer from intentions, attitudes, and experience

1990 ◽  
Vol 9 (1) ◽  
pp. 75-83 ◽  
Author(s):  
Meni Koslowsky ◽  
Michael Hoffman ◽  
Aryeh Lazar
Keyword(s):  
Predicting Behavior

Socially-Competent Computing Implementing Social Sensor Design

2012 ◽  
Vol 7 (3) ◽  
pp. 61-70 ◽  
Author(s):  
Maya Dimitrova
Keyword(s):  
Communication Networks ◽  
Real Data ◽  
Sensor Design ◽  
Dynamic Interactions ◽  
Medical Interventions ◽  
Human Interactions ◽  
Proposed Model ◽  
Predicting Behavior ◽  
Level Of Understanding ◽  
Social Sensor

The paper presents a conceptual model for social sensor design in socially-competent computing systems. The model is based on theories of social behavior being driven by the underlying attitudes, rather than on models predicting behavior in response to behavior representing people as physical objects in dynamic interactions. It is proposed to increase the ability of the systems to extract relevant features and to achieve better social competence, similar to the kind that is underlying human interactions by implementing algorithms, capable of predicting behavior in response to attitude. The paper presents an account of the social level of understanding human interactions in the context of three application scenarios – multi-hop communication networks, embedded systems for support of medical interventions and information systems supporting educational activities. Patterns of real data are discussed in terms of the proposed model of social sensor design for enhanced socially-competent computing.

Heterogeneity of Samples as a Problem in Shelf-Life Prediction

1983 ◽  
Vol 46 (6) ◽  
pp. 542-544 ◽  
Author(s):  
R. B. MAXCY ◽  
S. E. WALLEN
Keyword(s):  
Shelf Life ◽  
Common Assumption ◽  
Typical Sample ◽  
Shelf Life Prediction ◽  
Standard Plate ◽  
Plate Counts ◽  
Predicting Behavior ◽  
The Common ◽  
Multiple Samples ◽  
Sample Set

A study of the common assumption that a single package of milk represents the production lot was made. Sensory and microbial observations on shelf-life were of sample sets of sequentially produced cartons of milk from four different commercial operations. Neither spoilage rate nor nature of spoilage was uniform for a typical sample set of ten units. A single package, therefore, provided low probability for predicting behavior of the entire production lot. Observations on individual colonies from standard plate counts at the time of sensory spoilage indicated the microflora to be a pure culture in each spoiled unit. The extreme differences in spoilage rates of individual units within sample sets indicated sensory evaluation of multiple samples to be the most logical, simple criterion for evaluating shelf-life. The size of the sample set to be observed and frequency of sampling awaits further observations and application of statistical techniques to establish the accuracy of estimates desired.

scholarly journals What Works for Whom? Investigating Adolescents’ Pro-Environmental Behaviors

2020 ◽  
Vol 12 (18) ◽  
pp. 7313
Author(s):  
Mykolas Simas Poškus
Keyword(s):  
Big Five ◽  
Water Conservation ◽  
Sustainable Consumption ◽  
Sustainable Transportation ◽  
Extended Model ◽  
Environmental Behaviors ◽  
What Works ◽  
Clustering Approach ◽  
Predicting Behavior ◽  
Electricity Conservation

Pro-environmental behavior has been extensively studied using general models of predicting behavior; however, these models have very limited value in making inferences about individuals. To address this shortcoming, a person-oriented investigation of five pro-environmental behaviors differing in complexity was carried out using a clustering approach. A total of 863 adolescents (mean age 15.72 (SD = 1.1), 53.5% female) filled in the Big Five Inventory and measures of recycling, water conservation, electricity conservation, sustainable consumption, and sustainable transportation use based on an extended model of the Theory of Planned Behavior (TPB). TPB models were investigated in empirically derived clusters of individuals that differ by their personality traits. The results suggest that individuals in different personality clusters could be reached effectively through different means when trying to promote pro-environmental behaviors and different pro-environmental behaviors should not be regarded as homogeneous.

Explaining and Predicting Behavior

1991 ◽  
pp. 50-70
Author(s):  
Stuart S. Nagel
Keyword(s):  
Predicting Behavior
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