Jennifer M. Logg - Faculty & Research - Harvard Business School
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Jennifer M. Logg

Post-Doctoral Fellow of Business Administration

Negotiation, Organizations & Markets

Jennifer Logg is a Post-Doctoral Fellow in the Negotiation, Organizations & Markets Unit at Harvard Business School.  She studies how people can improve the accuracy of their decisions.  Specifically, her work examines when people are most likely to leverage the power of algorithms to improve their accuracy. Her other work tests factors that exacerbate overconfidence, people's overly positive beliefs about themselves, and whether unrealistic optimism helps performance as much as people think it does. She received her Ph.D. in Management of Organizations from the Haas School of Business at the University of California, Berkeley.

Journal Articles
  1. (Too) Optimistic about Optimism: The Belief that Optimism Improves Performance.

    Elizabeth R. Tenney, Jennifer M. Logg and Don A Moore

    A series of experiments investigated why people value optimism and whether they are right to do so. In Experiments 1A and 1B, participants prescribed more optimism for someone implementing decisions than for someone deliberating, indicating that people prescribe optimism selectively, when it can affect performance. Furthermore, participants believed optimism improved outcomes when a person’s actions had considerable, rather than little, influence over the outcome (Experiment 2). Experiments 3 and 4 tested the accuracy of this belief; optimism improved persistence, but it did not improve performance as much as participants expected. Experiments 5A and 5B found that participants overestimated the relationship between optimism and performance even when their focus was not on optimism exclusively. In summary, people prescribe optimism when they believe it has the opportunity to improve the chance of success—unfortunately, people may be overly optimistic about just how much optimism can do.

    Keywords: optimism; bias; accuracy; decision phase; Performance; Attitudes; Performance Improvement; Perception; Outcome or Result;

    Citation:

    Tenney, Elizabeth R., Jennifer M. Logg, and Don A Moore. "(Too) Optimistic about Optimism: The Belief that Optimism Improves Performance." Journal of Personality and Social Psychology 108, no. 3 (March 2015): 377–399. (lead article.)  View Details
Working Papers
  1. Is Overconfidence a Motivated Bias? Experimental Evidence

    Jennifer M. Logg, Uriel Haran and Don A. Moore

    Are overconfident beliefs driven by the motivation to view oneself positively? We test the relationship between motivation and overconfidence using two distinct, but often conflated, measures: better-than-average (BTA) beliefs and overplacement. Our results suggest that motivation can indeed affect overconfidence, but only under limited conditions. We find that motivation does indeed inflate BTA beliefs. However, introducing some specificity and clarity to the standards of assessment (Experiment 1) or to the trait’s definition (Experiments 2 and 3) reduces or eliminates this bias in judgment. We find stronger support for a cognitive explanation for overconfidence, which emphasizes the effect of task difficulty. The difficulty of possessing a desirable trait (Experiment 4) or succeeding on math and logic problems (Experiment 5) affected overconfidence in ways that are consistent with the cognitive account proposed by prior research, above and beyond motivation. Finally, we find the lack of an objective standard for vague traits allows people to create idiosyncratic definitions and view themselves as better than others in their own unique ways (Experiment 6). Overall, the results suggest motivation’s effect on overconfidence is driven more by idiosyncratic construals of assessment than by self-enhancing delusion. They also suggest that by focusing on vague measures (BTA rather than overplacement measures) and vague traits, prior research may have exaggerated the role of motivation in overconfidence.

    Keywords: self-perception; overconfidence; motivation; Better-Than-Average effect; specifically; Personal Characteristics; Perception; Motivation and Incentives; Cognition and Thinking;

    Citation:

    Logg, Jennifer M., Uriel Haran, and Don A. Moore. "Is Overconfidence a Motivated Bias? Experimental Evidence." Harvard Business School Working Paper, No. 18-099, April 2018.  View Details
  2. Seeker Beware: The Interpersonal Costs of Ignoring Advice

    Hayley Blunden, Jennifer M. Logg, Alison Wood Brooks, Leslie John and Francesca Gino

    Prior advice research has focused on understanding when and why people rely on (or ignore) advice and how this impacts judgment accuracy; little is known about the interpersonal consequences of the advice-seeking process. In this paper, we investigate the interpersonal consequences when an advisor believes his or her advice will be ignored. We find that advisors interpersonally penalize seekers perceived to ignore their advice because such dismissal threatens advisors’ sense of self-worth, leading them to judge seekers more harshly. Moreover, these effects are compounded by advisor expertise: expert advisors are more likely to punish seekers who ignore their advice than are non-expert advisors. We further find this effect drives advisor reactions to one of the most widely recommended advice-seeking strategies: seeking advice from multiple advisors to leverage the wisdom of crowds. Advisors negatively judge and interpersonally distance themselves from seekers who they learn consulted others, an effect which is mediated by perceptions that their own advice will not be followed. Advice seekers fail to anticipate this negative relational impact, exposing them to unanticipated adverse consequences of their advice-seeking decisions. These findings challenge previous recommendations for optimal advice seeking behavior.

    Keywords: advice; advice seeking; expertise; impression management; wisdom of crowds; Interpersonal Communication; Behavior; Experience and Expertise; Perception; Judgments;

    Citation:

    Blunden, Hayley, Jennifer M. Logg, Alison Wood Brooks, Leslie John, and Francesca Gino. "Seeker Beware: The Interpersonal Costs of Ignoring Advice." Harvard Business School Working Paper, No. 18-084, February 2018. (Revised April 2018.)  View Details
  3. Theory of Machine: When Do People Rely on Algorithms?

    Jennifer M. Logg

    Algorithms—scripts for mathematical calculations—are powerful. Even though algorithms often outperform human judgment, people resist allowing a numerical formula to make decisions for them (Dawes, 1979). Nevertheless, people increasingly depend on algorithms to inform their decisions. Eight experiments examined trust in algorithms. Experiments 1A and 1B found that advice influenced participants more when they thought it came from an algorithm than when they thought it came from other people. This effect was robust to presenting the advisor jointly or separately (Experiment 2). Experiment 3 tested a moderator: excessive confidence in one’s own knowledge attenuated reliance on algorithms. These tests are important because participants can improve their accuracy by relying more on algorithms (Experiment 4). Experiments 5 and 6 tested a mechanism for reliance: subjectivity of the decision. For objective decisions, participants preferred algorithmic advice, and for subjective decisions, participants preferred advice from people. Experiment 6 tested the interaction of subjectivity and the availability of expert advice. Participants preferred an expert to an algorithm, regardless of the domain (Experiment 6). Experiment 7 examined how decision makers’ own expertise influenced reliance on algorithms. Experts in national security, who regularly make forecasts, relied less on algorithmic advice than lay people did. These results shed light on the important question of when people rely on algorithmic advice over advice from people and have implications for the use of technological algorithms.

    Keywords: algorithms; accuracy; decision making; advice taking; Forecasting; Mathematical Methods; Decision Making; Forecasting and Prediction; Trust;

    Citation:

    Logg, Jennifer M. "Theory of Machine: When Do People Rely on Algorithms?" Harvard Business School Working Paper, No. 17-086, March 2017.  View Details