Featured Supported Projects

This project analyzes factors that influence repeated choices between two options, each of which has two possible outcomes. The options differ in their expected value, which is not known to the participant in advance. The project includes three experiments in which several factors were manipulated.

Goal

The project aims to determine the relative contribution of each manipulated factor to a participant’s subsequent choice; it can also define whether the outcome of a choice could have caused disappointment and/or regret.

Researchers

• Yaakov Kareev
• Judith Avrahami
• Maor Moshe

What we did

In progress. Data were collected, key variables include the characteristics of each option (possible values and their probabilities), the participant’s choice, the observed outcome (translated into gain or loss), the counterfactual outcome that would have occurred if the participant had chosen the other option in that round, and the participant’s subsequent choice.

Goal

To understand the relationship between public policy and feelings of belonging, loneliness, and discrimination among single people - a population not yet deeply explored using empirical methods.

Researchers

• Elyakim Kislev
• Maor Moshe

What we did

Using a groundbreaking and globally pioneering survey conducted by Professor Kislev, which included thousands of single men and women and sampled their attitudes toward public policy, we performed an analysis that established the survey's reliability and causal validity.

This project addresses complaints regarding freedom of expression in an academic institution during wartime. The dataset consists of four types of data: a file with details of all complaints, public organizational documents, internal organizational correspondence (WhatsApp), and legal correspondences.

Goal

The project’s goal is dealing with complaints regarding freedom of expression in an academic institution during wartime.

Researchers

• Tammy Lifschitz
• Or Rappel-Kroyzer

What we did

In progress.

This project develops person-centered models of walkability for vulnerable urban populations. The research combines a large-scale survey (about 1,000 participants) with spatial mapping of neighborhood characteristics and individual-level attributes, and aims to apply AI methods to predict the level of walkability that a specific person is likely to experience in a given neighborhood. Results will be cross-referenced with existing activity datasets to validate and refine predictions.

Goal

The project aims to build personalized walkability models that estimate, for each individual, the likelihood they will walk versus use other transport modes under specific environmental conditions by mapping spatial neighborhood characteristics alongside individual-level features and applying artificial-intelligence methods to predict a person’s walkability level; it will use a comprehensive survey of roughly 1,000 participants and cross-reference the survey results with existing activity datasets to evaluate and refine the predictive models and to identify the environmental and personal drivers of walkability for vulnerable populations.

Researchers

• Michal Farkash
• Or Rappel-Kroyzer

What we did

In progress.

This project aims to map and characterize “hot spots” of chronic pain using self-reported body-map data. The dataset includes about 200 community participants who meet chronic-pain inclusion criteria; each participant marks painful segments out of 74 body segments and assigns an intensity [0–10] to marked regions (0 used also for unmarked regions).

Goal

1) To characterize "hot spots" of pain within patterns of pain distribution and intensity (self-reported on body maps). 2) To identify clusters of people with chronic pain who share similar patterns of pain "hot spots" and/or similar distributions of pain intensity across the body. 3) To examine whether these clusters are meaningful with respect to demographic, clinical, and psychological measures, controlling for the known effect that a greater number of painful body regions is associated with worse pain outcomes.

Researchers

• Gadi Gilam
• Or Rappel-Kroyzer

What we did

In progress.