Ido Erev

JAIR Journal 2022 Journal Article

Predicting Decisions in Language Based Persuasion Games

• Reut Apel
• Ido Erev
• Roi Reichart
• Moshe Tennenholtz

Sender-receiver interactions, and specifically persuasion games, are widely researched in economic modeling and artificial intelligence, and serve as a solid foundation for powerful applications. However, in the classic persuasion games setting, the messages sent from the expert to the decision-maker are abstract or well-structured application-specific signals rather than natural (human) language messages, although natural language is a very common communication signal in real-world persuasion setups. This paper addresses the use of natural language in persuasion games, exploring its impact on the decisions made by the players and aiming to construct effective models for the prediction of these decisions. For this purpose, we conduct an online repeated interaction experiment. At each trial of the interaction, an informed expert aims to sell an uninformed decision-maker a vacation in a hotel, by sending her a review that describes the hotel. While the expert is exposed to several scored reviews, the decision-maker observes only the single review sent by the expert, and her payoff in case she chooses to take the hotel is a random draw from the review score distribution available to the expert only. The expert’s payoff, in turn, depends on the number of times the decision-maker chooses the hotel. We also compare the behavioral patterns in this experiment to the equivalent patterns in similar experiments where the communication is based on the numerical values of the reviews rather than the reviews’ text, and observe substantial differences which can be explained through an equilibrium analysis of the game. We consider a number of modeling approaches for our verbal communication setup, differing from each other in the model type (deep neural network (DNN) vs. linear classifier), the type of features used by the model (textual, behavioral or both) and the source of the textual features (DNN-based vs. hand-crafted). Our results demonstrate that given a prefix of the interaction sequence, our models can predict the future decisions of the decision-maker, particularly when a sequential modeling approach and hand-crafted textual features are applied. Further analysis of the hand-crafted textual features allows us to make initial observations about the aspects of text that drive decision making in our setup.

RLDM Conference 2019 Conference Abstract

The Effect of Experience on Clicking Decisions

• Ido Erev

The effort to predict the effect of experience on clicking decisions reveals several phenomena that appear to contradict basic reinforcement-learning models. These phenomena include: underweighting of rare events (Barron & Erev, 2003), the payoff variability effect (Busemeyer & Townsend, 1993), the wavy recency effect (Plonsky et al. , 2015), the big eyes effect (Erev & Rapoport, 1998), surprise-trigger-change (Nevo & Erev, 2012), and sensitivity to irrelevant alternatives (Spektor et al. , 2018; Erev & Roth, 2019). The current talk describes these phenomena, and announces a new choice prediction competition designed to compare alternative methods to predict the effect of experience on clicking decisions.

AAAI Conference 2017 Conference Paper

Psychological Forest: Predicting Human Behavior

• Ori Plonsky
• Ido Erev
• Tamir Hazan
• Moshe Tennenholtz

We introduce a synergetic approach incorporating psychological theories and data science in service of predicting human behavior. Our method harnesses psychological theories to extract rigorous features to a data science algorithm. We demonstrate that this approach can be extremely powerful in a fundamental human choice setting. In particular, a random forest algorithm that makes use of psychological features that we derive, dubbed psychological forest, leads to prediction that significantly outperforms best practices in a choice prediction competition. Our results also suggest that this integrative approach is vital for data science tools to perform reasonably well on the data. Finally, we discuss how social scientists can learn from using this approach and conclude that integrating social and data science practices is a highly fruitful path for future research of human behavior.

AIJ Journal 2007 Journal Article

Multi-agent learning and the descriptive value of simple models

• Ido Erev
• Alvin E. Roth