Decision making
A crucial requirement for effective human-AI teaming is the ability to form mutually compatible mental models that help humans discern when the AI can be relied upon and how to best complement each other. To understand the impact of having this mutual understanding, we conducted an experiment where participants (N=125), tasked as disaster relief planners, were assisted by an AI agent that recommends optimal allocation of a resource based on several information sources. The interaction effects of mental models between the human and the AI were studied.
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A viewer's existing beliefs can prevent accurate reasoning with data visualizations. In particular, confirmation bias can cause people to overweigh information that confirms their beliefs, and dismiss information that disconfirms them. We tested whether confirmation bias exists when people reason with visualized data and whether certain visualization designs can elicit less biased reasoning strategies. We asked crowdworkers to solve reasoning problems that had the potential to evoke both poor reasoning strategies and confirmation bias.
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This dataset is in support of my following Research papers
Preprint (Make sure you have read Caution) :
- Novel ß Transtibial Prosthetic 9-DoF Artificial Leg Adaptive Controller - Part I*
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Decision-makers across many professions are often required to make multi-objective decisions over increasingly larger volumes of data with several competing criteria. Data visualization is a powerful tool for exploring these complex ‘solution spaces’, but there is little research on its ability to support multi-objective decisions. In this paper, we explore the effects of visualization design and data volume on decision quality in multi-objective scenarios with complex trade-offs.
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This dataset refers to the case study performed in the paper "A Real Options Market-Based Approach to Increase Penetration of Renewables", submitted to IEEE Transactions on Smart Grid. The file contains the Midcontinent ISO data used for the day-ahead prices, as well as the wind data from NREL's Wind Integration National Dataset Toolkit which was used to estimate the renewable productions in the case study.
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There are 3 data files in total for this data set, 1 for Experiment 1 and 2 for Experiment 2. The File Experiment 1.csv contains 12 matrices for Experiment 1, which are the opinions of the decision makers with the pairwise comparison of alternatives in the form of the linguistic preference relations. The File Experiment 2-1.csv contains 51 matrices, which denote the opinions of the decision makers with the pairwise comparison of alternatives in the form of the Linguistic Discrete Region.
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