News

2024-4-30 :The project just finished! Time to wrap-up the papers and to write the final report!

2024-03-16: New publication in PNAS! (another one!!) Frequent winners explain apparent skewness preferences in experience-based decisions.  (with Sebastian Olschewski and Mikhail Spektor). Proceedings of the National Academy of Sciences of the United States of America (PNAS). Data in description-based decisions, investment decisions and choice theories (Prospect Theory) point at a preference for right-skewed options (small prob. of high $). In contrast, experience-based decisions point at an apparent preference for left-skewed distributions. We reconcile this puzzle by examining the effect of frequent winners on experience-based choice. Statistically, left-skewed reward distributions have the higher value in most direct comparisons of reward samples against a right-skewed distribution. We show in experience-based decision experiments that: 

1. People choose left-skewed over matched right-skewed options (while the left-skewed options are the frequent winners),

2. People choose frequently winning options,

3. When making the right-skewed options the frequent winners, people choose the right-skewed options over the left-skewed ones,

4. When both options have the higher value in exactly the same number of reward samples, people choose the right-skewed options.

We reconcile experience-based decisions with the mainstream literature w.r.t. skewness preferences: Pairwise experience-based decisions stress the direct sample comparison and the choices for the frequent winners overshadow skewness preferences in this task.  In the paper, we also present a reinforcement learning model with tallying and skewness preferences to account for all observed behavior, and we discuss the the adaptivity of frequent winner as well as skewness preferences. 

2023 11 30: New publication in PNAS! Uncovering the semantics of concepts using GPT-4  (with Balázs Kovács,  Michael Hannan, & Guillem Pros). We use GPT-4 to create “typicality measures” that quantitatively assess how closely text documents align with a specific concept or category. Unlike previous methods that required extensive training on large text datasets, the GPT-4-based measures achieve state-of-the-art correlation with human judgments without such training. Because training data is not needed, this dramatically reduces the data requirements for obtaining high performing model-based typicality measures. Our analysis spans two domains: judging the typicality of books in literary genres and the typicality of tweets in the Democratic and Republican parties. Our results demonstrate that modern Large Language Models (LLMs) can be used for text analysis in the social sciences beyond simple classification or labelling.

2023 11 29: New working paper available on arXiv.org! Scaling Political Texts with ChatGPT (with Aina Gallego). We introduce an innovative methodology for positioning political texts in continuous policy spaces using GPT-4. We validate our approach in two ways. Firstly, using a standard dataset of party manifestos, our analysis reveals that the policy positions derived via GPT-4 exhibit a higher correlation with expert assessments than the positions produced by crowd workers and by other automated text analysis approaches based on word frequency. Secondly, by analyzing tweets from members of the US Congress, we show that the policy positions inferred by GPT-4 show a remarkable correlation (exceeding 90\%) with crowd-sourced estimates. These results demonstrate that GPT-4 is an efficient and valid method to scale text documents in latent political space.

2023-11-16: What if there was a 'dislike' button on Twitter? In a paper just published in PLOS One, with my former doctoral students Elizaveta Konovalova and Nikolas Schöll, we show that adding a 'dislike' button could make opinions expressed on social media less extreme. This is because, this would expose social media users to negative quantitative feedback that they do not usually get (because most social media platforms do not have a one-button negative feedback function such as a 'dislike' button). Using simulations of a learning model, we compare two feedback environments that differ in terms of the availability of negative reaction counts. We find that expressed opinions are generally more extreme when negative reaction counts are not available than when they are. We rely on analyses of Twitter data and several online experiments to provide empirical support for key model assumptions and test model predictions. Our findings suggest that a simple design change might limit, under certain conditions, the expression of extreme opinions on social media. 

2023-09: Cambridge University Press has published Sampling in Judgment and Decision Making, a new book edited by Klaus Fiedler, Peter Juslin and Jerker Denrell. Three chapters feature my work with Jerker Denrell (The Hot Stove Effect), Elizaveta Konovalova (Opinion Homogenization and Polarization: Three Sampling Models) and Balázs Kovács, Judith Avrahami & Yaakov Kareev (The Collective Hot Stove Effect). The European Research Council (ERC), Fundación BBVA and the Spanish Ministry of Science and Innovation are gratefully acknowledged for funding this research.

2023-03-23: How do politicians learn from the feedback they receive from citizens on social media? To learn more about this, check out my new paper in the American Journal of Political Science, with Nikolas Bahati Schöll and Aina Gallego. We used the BERT language model to identify the topic of 1.2M tweets by all elected Spanish politicians (local and national assemblies) over the 2016-19 election cycle and estimated a learning model on this naturally occurring data (think 'bandit problem in the wild'). We find that female politicians are more strongly rewarded by other Twitter users for writing about gender issues than male politicians and that this could contribute to explaining why they write more about this topic. Thanks to Guillem Pros Rius for excellent research assistance in putting together the replication material.

2022-11-10: Paper accepted in Sociological Science! Using Machine Learning to Uncover the Semantics of Concepts: How Well Do Typicality Measures Extracted from a BERT Text Classifier Match Human Judgments of Genre Typicality? (with Balázs Kovács,  Michael Hannan, & Guillem Pros). Forthcoming in Sociological Science.  [pre-print PDF, Publicly available and free-to-use Python Notebooks to compute typicality using Google Colab, and open data].

2022-11-01: Alexandros Gelastopoulos (Google Scholar) just joined my ERC team as a post-doc! I am looking forward to working more closely with him on topics that pertain to information sampling and collective behavior phenomena!

2022-08: My ERC project was granted an extension until April 30th, 2024. I am looking to hire a post-doc and a pre-doc researcher to work on the latest workpackages focusing on collective beliefs and behavior. I am looking for candidates with experience with Python programming, oTree, and deep learning packages like Tensorflow and Pytorch to implement transformers language models (e.g., BERT). See job offers here: 

• RA full time: https://euraxess.ec.europa.eu/jobs/818614

• RA part time: https://euraxess.ec.europa.eu/jobs/819267

• Postdoc full time: https://euraxess.ec.europa.eu/jobs/820454

• Postdoc part time: https://euraxess.ec.europa.eu/jobs/820481

2022-08: I am teaching at the International Rationality Summer Institute 2022. [IRSI3][Slides]

2022-06: I presented a methodological paper on using deep learning text classifiers to compute the typicality of objects. A draft paper, readme, and open access code are available on OSF here [https://osf.io/ta273/]. It is easy to run on the freely accessible Google Colab platform. Just dowload the 'compute_typicality' folder to your google drive and open the python notebook using Colab.

2022-05: I am co-organizing the summer school of the European Association of Decision Making in which we will teach content related to my ERC project to a set of about 30 motivated students from all over the world! 

2021-06: My current post-doctoral research Rahil Hosseini is on the academic jobmarket. Rahil is an excellent behavioral researcher who focuses on how people review products, services and other people online, and how they use online reviews to guide their decisions. Check her webpage here and her jobmarket paper here (that we are currently revising for re-submission at the Journal of Consumer Research). She is also a dedicated instructors and students love her teaching. UPDATE 2021-10: Rahil is taking a tenure track position at the Department of Business Administration at UC3M! Congratulations, Rahil!

2021-03: My paper with my former doctoral student Thomas Woiczyk was accepted for publication at the Journal of Personality and Social Psychology ! Evaluating Categories from Experience: The Simple Averaging Heuristic, Journal of Personality and Social Psychology, DOI: 10.1037/pspa0000231. 

Abstract: We analyze how people form evaluative judgments about categories based on their experiences with category members. Prior research suggests that such evaluative judgments depend on some experience average, but is unclear about the specific kind of average. We hypothesized that evaluations of categories could be driven either by the ‘simple average’ of experiences with the category or by the ‘member average’ (the average of the evaluations of the category members, where the evaluation of a category member is the average of experiences with this particular member). Under- standing whether evaluations of categories are driven by the ‘simple average’ or the ‘member average’ is important in settings where people obtain unbalanced numbers of observations about category members such as when people form opinions about a social group and predominantly interact with just a few members of this group. Across 9 studies (N=1,966), we consistently found that evaluative judgments about categories were better explained by the simple average than by the member average. We call the underlying cognitive strategy the ‘simple averaging heuristic.’ Collected evidence indicates that participants relied on simple averaging even in settings where normative principles required avoiding the use of this cognitive strategy, leading to systematic mistakes. Our findings contribute to several areas of social cognition such as research on redundancy biases, information aggregation, social sampling, and norm perceptions.

2019-06: My paper with my former doctoral student Elizaveta Konovalova was accepted for publication at Psychological Review (for me, the top journal in Psychology)! An Information Sampling Explanation for the In-Group Heterogeneity Effect, Psychological Review, 2020, 127(1), 47–73. DOI: 10.1037/rev0000160. [pre-print PDF and open data] . 

Abstract: People often perceive their in-groups as more heterogeneous than their out-groups. We propose an information sampling explanation for this in-group heterogeneity effect. We note that people frequently obtain larger samples of information about in-groups than about out-groups. Using computer simulations, we show that this asymmetry in sample sizes implies the in-group heterogeneity effect under a wide range of assumptions about how experience affects perceived variability. This is the case even when perceived variability is the outcome of rational information processing, implying that the structure of the environment is sufficient to explain the emergence of the in-group heterogeneity effect. A key assumption of our explanation is that perceived group variability depends on the size of the sample observed about this group. We provide evidence in support for this assumption in two experiments. Our results considerably expand the scope and relevance of a prior sampling explanation proposed by Linville, Fischer, and Salovey (1989). They also complement other explanations that proposed that information about in-groups and out-groups is processed differently.

2019-05: Call for applications for post-doctoral researchers to work on the ERC project (Starting date: between July and September 2019). Click here for details and here for the application form. 

2019-02: Our paper describing the surprising few-get-richer effect has been accepted at the Web Conference 2019, to be held in San Francisco, May 13-17, 2019!

The few-get-richer: a surprising consequence of popularity-based rankings, (with Fabrizio Germano & Vicenç Gómez), accepted at Web Conference, 2019. [Arxiv].