About

Eli J. Finkel is a professor at Northwestern University with appointments in the psychology department at the Weinberg College of Arts & Sciences and the Kellogg School of Management. His research spans a range of topics including marriage, interpersonal attraction, conflict, forgiveness, self-control, and social interaction, with a focus on understanding the predictors and dynamics of relationships and social behavior. He is the author of The All-or-Nothing Marriage, a co-host of the Love Factually podcast, and a guest essayist for The New York Times. His work has been recognized by The Economist as that of a leading figure in relationship psychology. Finkel's academic background includes a PhD in Social & Quantitative Psychology from the University of North Carolina at Chapel Hill, along with a master's degree from the same institution and a bachelor's degree in Psychology from Northwestern University. He has held positions as a Professor of Management and Organizations at Kellogg and as a Professor of Psychology at Northwestern, with previous roles as an Associate and Assistant Professor. His research interests include interpersonal attraction, conflict, forgiveness, and self-control, and he teaches courses related to negotiation, relationships, and psychology. Finkel has received numerous awards for his research and teaching, and he serves in editorial roles for prominent psychology journals.

Research topics

• Political Science
• Psychology
• Sociology
• Social psychology
• Social Science
• Public relations
• Medicine
• Computer Science
• Psychiatry
• Law
• Artificial Intelligence
• Engineering
• Statistics
• Physics
• Mathematics
• Clinical psychology
• Virology
• Developmental psychology
• Psychoanalysis

Selected publications

• Interdependence in shared goal pursuit among established and impromptu dyads: Expectations, allocation, and performance

  Frontiers in Social Psychology · 2025-03-11 · 1 citations

  articleOpen access

  Introduction Transactive goal dynamics theory asserts that interdependent partners have opportunities and motivation to learn about each other's idiosyncratic skills and interests in goal pursuit, producing enhanced system-level knowledge and performance. These shared knowledge structures of each other's skills and preferences should produce more efficient allocation of tasks to complete in goal pursuit. The present study directly tests this hypothesis using an empirical demonstration that allows for a comparison of shared goal pursuit among couples with experimentally manipulated interdependence. Specifically, we examined how people allocated and subsequently completed individual tasks toward a shared outcome when working with an established partner compared to working with an impromptu gender-matched partner. Method To accomplish these aims, we recruited two pairs of romantic partners to complete a laboratory session. Each couple was randomly assigned to complete a series of tasks as part of either an established dyad (i.e., couples worked together) or impromptu dyad (i.e., couples traded partners). Results Established dyads (a) considered the system's strengths in dividing tasks and (b) divided tasks more effectively than impromptu dyads. Established dyads also expected to and did perform better than impromptu dyads. Discussion These findings characterize how goal interdependence manifests in close relationships.

  PublisherOA PDFDOI

• No gender differences in attraction to young partners: A study of 4,500 blind dates

  Proceedings of the National Academy of Sciences · 2025-01-27 · 4 citations

  articleOpen access

  In mixed-gender couples, men are older than women on average. Scholars and laypeople presume that this arrangement reflects mirrored preferences such that men desire younger partners and women desire older partners. Nevertheless, relevant published data on in-person romantic evaluations—that is, studies where adults interact in person and report their initial attraction to each other—are nearly nonexistent. We examined the association of a partner’s age with romantic desire ( N = 9,084 dyadic reports) among N = 6,262 blind daters who used a matchmaking service in hopes of finding a long-term partner. Preregistered tests revealed that people were (slightly) attracted to younger partners on average—and this association did not differ by gender. Conclusions were identical if we examined a) age difference from one’s own age, and b) a dataset limited to women 40-and-under and mixed-gender dates. Furthermore, participant’s self-reported “upper-age limits” played no meaningful role: Participants had a modest preference for youth overall, but it did not matter whether the partner’s age fell below or above this personal maximum. We discuss the implications of the nonexistent initial-attraction gender difference for the age difference in mixed-gender couples.

  PublisherOA PDFDOI

• Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

  UNC Libraries · 2025-06-26

  articleOpen access

  Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner's ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person's own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

  PublisherDOI

• Moralizing partisanship when surrounded by copartisans versus in mixed company

  PNAS Nexus · 2025-03-27

  articleOpen accessSenior author

  ) express these partisan moralization views. Critically, we compare the rates of partisan moralization not only when users are in contexts (subreddits) of their ingroup (e.g. r/democrats, r/vegetarian, r/Conservative, r/Hunting) but also when in mixed-company contexts populated mostly by users without partisan engagement (e.g. r/Music, r/Parenting). First, we developed four word embedding models-two for the users of each political side, one based on their comments in their ingroup contexts and one based on their comments in mixed-company contexts. Then, we evaluated the words of each model on two semantic dimensions, partisanship and morality, and we examined their correlation as an indicator of the expressed partisan moralization. Our first analysis demonstrated that LW users express moralized partisanship to a similar degree when surrounded by copartisans and when in mixed company. However, the moralized partisanship expressed by RW users in mixed company is weaker than that they express among copartisans, as well as that expressed by LW users in mixed company. In a second analysis, we divided partisan contexts based on whether they are inherently political (e.g. r/democrats) or not (e.g. r/vegetarian). This second analysis revealed that RW users express moralized partisanship more strongly than LW users in inherently political contexts, but right- and left-wingers are similar in nonpolitical partisan contexts. The discussion considers potential explanations for these asymmetries.

  PublisherOA PDFDOI

• Romantic Relationships

  2025-05-01 · 1 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Partisan Antipathy and the Erosion of Democratic Norms

  2024-01-25 · 10 citations

  preprintOpen access

  Does partisan antipathy undermine democracy? Over the past decade, claims of such an effect have pervaded both popular and scholarly discourse—to the point where the link has become widely accepted as an article of faith. But an impressive stream of new studies has raised credible doubts about whether such an effect actually exists. In the present report, even as we replicate the null effects in that nascent literature, we demonstrate that partisan antipathy, when properly conceptualized and operationalized, does indeed predict antidemocratic tendencies. Leveraging cross-sectional, longitudinal, and experimental methods, including a study conducted during America’s 2022 Midterm Elections, we developed and validated a measure of political sectarianism (a blend of othering, aversion, and moralization toward opposing partisans), which robustly predicted antidemocratic tendencies. In contrast, the influential construct of affective polarization (assessed in terms of cold feelings toward opposing partisans) did not. These findings have important implications for theory and measurement in the social sciences, for understanding democratic erosion, and for applied efforts to bolster American democracy by bridging partisan divides.

  PublisherOA PDFDOI

• Can Large Language Models Detect Verbal Indicators of Romantic Attraction?

  arXiv (Cornell University) · 2024-06-23 · 1 citations

  preprintOpen accessSenior author

  As artificial intelligence (AI) models become an integral part of everyday life, our interactions with them shift from purely functional exchanges to more relational experiences. For these experiences to be successful, artificial agents need to be able to detect and interpret social cues and interpersonal dynamics; both within and outside of their own human-agent relationships. In this paper, we explore whether AI models can accurately decode one of the arguably most important but complex social signals: romantic attraction. Specifically, we test whether Large Language Models can detect romantic attraction during brief getting-to-know-you interactions between humans. Examining data from 964 speed dates, we show that ChatGPT can predict both objective and subjective indicators of speed dating success (r=0.12-0.23). Although predictive performance remains relatively low, ChatGPT's predictions of actual matching (i.e., the exchange of contact information) were not only on par with those of human judges but incremental to speed daters' own predictions. In addition, ChatGPT's judgments showed substantial overlap with those made by human observers (r=0.21-0.35), highlighting similarities in their representation of romantic attraction that are independent of accuracy. Our findings also offer insights into how ChatGPT arrives at its predictions and the mistakes it makes. Specifically, we use a Brunswik lens approach to identify the linguistic and conversational cues utilized by ChatGPT (and human judges) vis-a-vis those that are predictive of actual matching.

  PublisherOA PDFDOI

• Organizational Consequences of Misperceptions about Sensitive Topics

  Academy of Management Proceedings · 2024-07-09

  article

  Conversations addressing conflicts, disagreements, and sensitive topics are instrumental for both individual and team decision-making in organizational settings. Nevertheless, discussions of difficult or sensitive topics are often avoided due to a common misconception that such dialogues diminish decision-making efficiency, exacerbate conflicts, and strain relationships. In this symposium, we present novel research on organizational and interpersonal contexts where people fail to talk about and effectively manage sensitive topics. These topics are often controversial, including the request to initiate a negotiation, changing one’s political views, and engaging with large-scale societal problems through reporting or helping. In particular, the papers presented will show that people (1) overestimate how likely negotiation counterparts are to withdraw a deal if one attempts to negotiate, and as a result, avoid negotiating; (2) overestimate how likely ingroup members are to penalize one for changing one’s mind about controversial political topics, which leads to self-censorship; (3) have conflicting perceptions of victims’ motivations in reporting about similar events, which affects trust and perceptions of accuracy; (4) underestimate the sensitivity and impact of big problems, leading to lower helping; (5) may overestimate the mere effect of apologies on reducing medical lawsuits. Moreover, this set of papers shows the detrimental consequences of such misperceptions, particularly for missed opportunities for disclosure and for economic and relational benefits. Taken together, this symposium highlights the fraught nature of sensitive topics, and points to avenues for improving the effective flow of information within organizations. Negotiators’ Inflated Perception of Their Likelihood of Jeopardizing a Deal Author: Einav Hart; George Mason U. Author: Julia Bear; Stony Brook U.-State U. of New York Author: Zhiying Ren; The Wharton School, U. of Pennsylvania Intragroup Illusions: Overestimating the Social Costs of Political Belief Change Author: Trevor Spelman; Northwestern Kellogg School of Management Author: Abdo Elnakouri; Northwestern U. Author: Nour Kteily; Northwestern Kellogg School of Management Author: Eli Finkel; Kellogg School of Management, Northwestern U. Motivated to Uncover the Truth: When Past Experiences of Victimization Boost Trust Author: Jennifer Abel; Harvard Business School Author: Julian Jake Zlatev; Harvard Business School The Bigger the Problem the Littler Author: Lauren Eskreis-Winkler; Northwestern Kellogg School of Management Author: Luiza Peres; Kellogg School of Management, Northwestern U. Author: Ayelet Fishbach; professor Apologies: Is Their Effect in Reducing Lawsuits for Medical Malpractice a Misperception? Author: Nelly Arbel Groissman; Technion - Israel Institute of Technology Author: Eran Dorfman; Technion - Israel Institute of Technology Author: Elad Yom Tov; Bar Ilan U. Author: Paul Feigin; Technion - Israel Institute of Technology Author: Anat Rafaeli; Technion Israel Institute of Technology

  PublisherDOI

• The delusion of the disappearing self? Attachment avoidance and the experience of externally invisible self-loss in romantic relationships.

  Journal of Personality and Social Psychology · 2024-09-19 · 4 citations

  articleSenior author

  All of us experience self-change in relationships, but our subjective experiences of change may not always align with external metrics of such change. We hypothesized that people with higher attachment avoidance are more likely to experience self-change as a loss, which in turn predicts lower relationship commitment. We further hypothesized, however, that there would be a disparity in perceptions, such that avoidant people will experience self-loss that external metrics-including their own behaviors and ratings from third-party coders-do not detect. Results from four studies, which employed a variety of cross-sectional (Studies 1 and 4) and longitudinal (Studies 2 and 3) methods, demonstrated that higher attachment avoidance predicted greater experienced loss of self, which in turn predicted lower commitment. Studies 2-4 also revealed evidence for the hypothesized disparity in perceptions: Avoidantly attached individuals' experience of greater self-loss failed to emerge when using a variety of external metrics of self-loss, producing Avoidance × Loss Type (experienced vs. external metric) interaction effects. These studies suggest that avoidantly attached people, who tend to be vigilant to autonomy threats in relationships, experience relationship-linked changes as losses, even though external metrics fail to detect such loss. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

  PublisherDOI

• Do Large Language Models Understand Verbal Indicators of Romantic Attraction?

  2024-06-23 · 1 citations

  preprintOpen accessSenior author

  What makes people “click” on a first date and become mutually attracted to one another? While understanding and predicting the dynamics of romantic interactions used to be exclusive to human judgment, we show that Large Language Models (LLMs) can detect romantic attraction during brief getting-to-know-you interactions. Examining data from 964 speed dates, we show that ChatGPT (and Claude 3) can predict both objective and subjective indicators of speed dating success (r=0.12-0.23). ChatGPT’s predictions of actual matching (i.e., the exchange of contact information) were not only on par with those of human judges who had access to the same information but incremental to speed daters’ own predictions. While some of the variance in ChatGPT’s predictions can be explained by common content dimensions – such as the valence of the conversations – the fact that there remains a substantial proportion of unexplained variance suggests that ChatGPT also picks up on conversational dynamics. In addition, ChatGPT’s judgments showed substantial overlap with those made by the human observers (mean r=0.29), highlighting similarities in their representation of romantic attraction that is, partially, independent of accuracy.

  PublisherOA PDFDOI

Recent grants

• Advancing Understanding About One Form of Interpersonal Violence

  NSF · $492k · 2007–2012

Frequent coauthors

• Paul W. Eastwick

  University of California, Davis

  76 shared

• James N. Druckman

  University of Rochester

  42 shared

• Jay Joseph Van Bavel

  Norwegian School of Economics

  42 shared

• Rick H. Hoyle

  38 shared

• Alexander Landry

  36 shared

• Caryl E. Rusbult

  Vrije Universiteit Amsterdam

  33 shared

• Madoka Kumashiro

  32 shared

• Laura B. Luchies

  Healthwise

  20 shared

Education

• Ph.D., Social Psychology

  Northwestern University

  2002

• M.A., Social Psychology

  Northwestern University

  1998

• B.A., Psychology

  University of California, Los Angeles

  1995

Awards & honors

• Journal of Consumer Research "Editors' Choice" recognition f…
• Northwestern Undergraduate Students, “Faculty Honor Roll” (“…
• Weinberg College of Arts and Sciences’ “Distinguished Teachi…
• Martin J. and Patricia Koldyke Outstanding Teaching Professo…
• George A. Miller Award for an Outstanding Recent Article on…