skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Toward the Development of a Revised Decision-Making Competency Instrument
This research paper discusses the process of refining and expanding the Decision-Making Competency Inventory developed by Miller and Byrnes. Byrnes is the author of the Self-Regulation Model of Decision-Making (SRMDM), which posits that that self-regulated decision-makers spend time in three phases: generation of options, evaluation of options, and learning from the results. Additionally, adaptive decision-makers are aware of moderating factors (such as stress or lack of information) and work to overcome them. A revised instrument is presented.  more » « less
Award ID(s):
1745347
PAR ID:
10213555
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2018 ASEE Annual Conference & Exposition Proceedings
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, Behavioural Public Policy)
    null (Ed.)
    Although the need for urgent climate change action is clear, insights about how to make better climate risk management decisions are limited. While significant attention from behavioral researchers has focused on choice architecture, we argue that many of the contexts for addressing climate risks require increased attention to the needs of a deliberative and dynamic choice environment. A key facet of this kind of decision is the need for decision-makers and stakeholders to identify and balance conflicting economic, social and environmental objectives. This recognition of difficult, context-specific trade-offs highlights the need for structuring the decision-making process so that objectives are clearly articulated and prioritized. Equally, policy analyses and deliberations must effectively link priorities with climate risk management options. This restructuring of decision-making about climate change calls for more than a nudge. Scientific and technical efforts must be redirected to help stakeholders and decision-makers better understand the diverse implications of climate change management alternatives and to become better equipped to take actions commensurate with the urgency of the problem. 
    more » « less
  2. (, Psychonomic Bulletin & Review)
    Abstract Theories of dynamic decision-making are typically built on evidence accumulation, which is modeled using racing accumulators or diffusion models that track a shifting balance of support over time. However, these two types of models are only two special cases of a more general evidence accumulation process where options correspond to directions in an accumulation space. Using this generalized evidence accumulation approach as a starting point, I identify four ways to discriminate between absolute-evidence and relative-evidence models. First, an experimenter can look at the information that decision-makers considered to identify whether there is a filtering of near-zero evidence samples, which is characteristic of a relative-evidence decision rule (e.g., diffusion decision model). Second, an experimenter can disentangle different components of drift rates by manipulating the discriminability of the two response options relative to the stimulus to delineate the balance of evidence from the total amount of evidence. Third, a modeler can use machine learning to classify a set of data according to its generative model. Finally, machine learning can also be used to directly estimate the geometric relationships between choice options. I illustrate these different approaches by applying them to data from an orientation-discrimination task, showing converging conclusions across all four methods in favor of accumulator-based representations of evidence during choice. These tools can clearly delineate absolute-evidence and relative-evidence models, and should be useful for comparing many other types of decision theories. 
    more » « less
  3. ; ; (, Proceedings of the ACM on Human-Computer Interaction)
    AI assistance in decision-making has become popular, yet people's inappropriate reliance on AI often leads to unsatisfactory human-AI collaboration performance. In this paper, through three pre-registered, randomized human subject experiments, we explore whether and how the provision of second opinions may affect decision-makers' behavior and performance in AI-assisted decision-making. We find that if both the AI model's decision recommendation and a second opinion are always presented together, decision-makers reduce their over-reliance on AI while increase their under-reliance on AI, regardless whether the second opinion is generated by a peer or another AI model. However, if decision-makers have the control to decide when to solicit a peer's second opinion, we find that their active solicitations of second opinions have the potential to mitigate over-reliance on AI without inducing increased under-reliance in some cases. We conclude by discussing the implications of our findings for promoting effective human-AI collaborations in decision-making. 
    more » « less
  4. ; ; ; ; ; ; ; (, International Journal of Wildland Fire)
    Background The decision making process undertaken during wildfire responses is complex and prone to uncertainty. In the US, decisions federal land managers make are influenced by numerous and often competing factors. Aims To assess and validate the presence of decision factors relevant to the wildfire decision making context that were previously known and to identify those that have emerged since the US federal wildfire policy was updated in 2009. Methods Interviews were conducted across the US while wildfires were actively burning to elucidate time-of-fire decision factors. Data were coded and thematically analysed. Key results Most previously known decision factors as well as numerous emergent factors were identified. Conclusions To contextualise decision factors within the decision making process, we offer a Wildfire Decision Framework that has value for policy makers seeking to improve decision making, managers improving their process and wildfire social science researchers. Implications Managers may gain a better understanding of their decision environment and use our framework as a tool to validate their deliberations. Researchers may use these data to help explain the various pressures and influences modern land and wildfire managers experience. Policy makers and agencies may take institutional steps to align the actions of their staff with desired wildfire outcomes. 
    more » « less
  5. ; ; ; ; ; ; (, IEEE)
    Smart cities seek to leverage data from advanced information, communication, and sensor technologies (ICSTs) for achieving their transportation-related sustainability goals. However, the multi-source, multi-timescale nature of these disparate data sets introduces many challenges to community decision-makers, hindering the use of these technologies in an efficient, effective, and holistic manner. Here, using statistical and machine learning methods, we present a visualization platform developed for the City of Peachtree Corners, GA, comprising nine integrated data sets. This platform can capture dynamic interactions between data from different sources and has the potential to support decision-makers in developing different solution options for contemporary transportation-related problems in a smart city environment. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email