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Understanding How Emergency Managers Evaluate Crowdsourced Data: A Trust Game-Based Approach
 

Understanding How Emergency Managers Evaluate Crowdsourced Data: A Trust Game-Based Approach

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Presentation of Kathleen Moore, Andrea H. Tapia and Christopher Griffin on the topic "Understanding How Emergency Managers Evaluate Crowdsourced Data: A Trust Game-Based Approach" at ISCRAM2013

Presentation of Kathleen Moore, Andrea H. Tapia and Christopher Griffin on the topic "Understanding How Emergency Managers Evaluate Crowdsourced Data: A Trust Game-Based Approach" at ISCRAM2013

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    Understanding How Emergency Managers Evaluate Crowdsourced Data: A Trust Game-Based Approach Understanding How Emergency Managers Evaluate Crowdsourced Data: A Trust Game-Based Approach Presentation Transcript

    • Understanding How Emergency Managers Evaluate Crowdsourced Data: A Trust Game-Based Approach Kathleen Moore Andrea H. Tapia Christopher Griffin The Pennsylvania State University College of Information Sciences & Technology ISCRAM 2013 – Baden-Baden, Germany
    • Problem Statement • Crowdsourced data by Emergency Managers (EM) has been a significant topic of scholarly discussion • Strongest barrier to use identified as data quality • Focus on data quality is a small part of problem space • Need to understand info behavior
    • Background • Role of EMs • Avoid and mitigate risk • Prepare, respond, recover from disasters • EM decision making • Hampered by stress of time • Cognitive limits of EM • Amount and quality of information • Not necessarily problematic as research has shown that even with all available information, managers do not always make optimal decision regarding events and can misinterpret information
    • From Data to Decisions • EMs overcome through satisficing • Choosing good-enough solutions over perfect ones • Social media a technical solution to enhance dissemination • Problem: flow, quality, usability, trustworthiness, verifiability • Overwhelms the process/EM • Any technical solutions must account for information culture of the organization/EM receiving the data
    • Assessing Trust in Data • Trust: acceptance of a certain amount of risk when lacking full knowledge and lacking the ability to fully control a situation (Alpern 1997) • Ability, benevolence, integrity • Fluid, dynamic, reiterative • Trust, mistrust, distrust, untrust • Better understood in static Web, ecommerce, old social media, less so in new social media • Primarily studied from perspective of information provider and not receptor
    • Trust & Social Media • Old rules/understandings do not apply • Trust IS NOT one, two, or even three data points • One Tweet may yield OVER 30 pieces of information! • Meredith Morris @ Microsoft • Social Cues are less understood • What is ability, benevolence, integrity in 140 characters? • To study trust in microblogging: • Person providing information • Quality, consistency of that information • ACT of the person ACCEPTING that information
    • Research Goal • Study EM as a trustworthy data analyst • Propose: develop model for capturing trust- analytical behavior through game theory and semantic content
    • Research Design • Phase I • Measuring trustworthy data, modeling trust behavior, and building a game • Non-cooperative, and Game Theory perspective • Game Theory (loosely) • System for analysis of behavior where consequences of actors’ decisions depend on information provided by others for EM to act upon • System to organize, capture and learn from future experience • Game setting creates sense of competitiveness/urgency to mimic stress of pressing events facing EM
    • The Math! • Beautiful! Elegant! • It will change what you have for breakfast! • Refer to paper in ISCRAM Proceedings • PLEASE email any questions!!
    • The Game • Multi-turn 2 player game • Player 1 – Teller (microblogger) • Player 2 – Actor (EM) • Player 2 does or does not act on info by Player 1 in a finite set of sentences • Every piece of information is either consistent with what we know, inconsistent or could be consistent, but no way to tell • Halting play occurs when Player 2 no longer wishes to accept information from Player 1 (deemed untrustworthy) • The objective of game is to obtain largest net payoff possible • Component of experimentation, develop tool to help EM detect less than credible information • Determine impact information has on quality of a player’s responses to stimuli
    • Game Format
    • Implications/Future Work • Develop models of how trust works in a microblogged environment providing crowdsourced data during crisis events • Will need to address veracity of trust factors in crowdsourced data as viewed by analysts rather than everyday persons/technical specialists • Also reconcile potential discrepancies between what is perceived as trustworthy versus what is acted upon given perception of trustworthy.
    • Thank You ISCRAM 2014 Kathleen Moore kam6015@psu.edu