AI as an Agent

Last registered on January 06, 2026

View Trial History

Pre-Trial

Trial Information

General Information

Title

AI as an Agent

RCT ID

AEARCTR-0016811

Initial registration date

September 18, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

September 19, 2025, 10:19 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated

January 06, 2026, 5:55 PM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

United States of America

Region

Primary Investigator

Name

Robert Innes

Affiliation

University of California Merced

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Tony Hua

PI Affiliation

University of California Merced

Contact Investigator

Additional Trial Information

Status

In development

Start date

2025-09-18

End date

2027-02-01

Keywords

Behavior

Additional Keywords

Artificial Intelligence, Agent, Social Preference

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

This study examines AI decision-making as an agent in choice experiments with a moral component. Questions of interest are: How are AI decisions affected by (1) posited objectives for AI (extent of representation of one person v. another), (2) the decision frame / game type, (3) observability of AI choices by affected person(s), (4) observability of the choice environment by affected parties, (5) preferences of affected person(s), and (6) whether AI is making a final decision or providing advice to affected parties? Treatments vary in all of these dimensions. Additional data will be collected on human responses to AI v. human advice.

External Link(s)

Registration Citation

Citation

Hua, Tony and Robert Innes. 2026. "AI as an Agent." AEA RCT Registry. January 06. https://doi.org/10.1257/rct.16811-1.2

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

AI (ChatGPT 5) will be prompted with choice frameworks that vary along the dimensions indicated in the abstract. AI will decide (or advise a decision) that affects interactions and payoffs between two human individuals (e.g., Sender and Receiver) in a choice experiment. AI will be acting on behalf of one of the two individuals (e.g., Sender) as an agent, but with an objective that may account for effects on the other individual (e.g., Receiver). AI decision responses and justifications will be recorded. With a sample of prompts, inferences will be drawn on effects of different dimensions on AI decisions and associated preferences. For example, in a dictator game, how do prompted objectives favoring the Dictator (v. Receiver) relatively more affect (1) the AI propensity for choosing a fair allocation on behalf of the Dictator and (2) AI's responsiveness to a revealed Dictator preference for a fair (v. selfish) allocation? Similar questions will be examined along the other dimensions of interest in this project.

Intervention Start Date

2025-09-18

Intervention End Date

2027-02-01

Primary Outcomes

Primary Outcomes (end points)

Outcomes are AI decisions in the posited choice games in the different treatments (varying the choice environment along the dimensions indicated in the abstract). For example, in a dictator game, outcomes will be the allocation choice of AI on behalf of the Dictator (where the allocation determines payments to Dictator and Receiver). We will be interested in how treatment variation drives variation in AI decisions.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

AI choice environments will be varied in two games with moral components. Treatment variation will include (1) game type, (2) AI decision v. AI advice, (3) information made available to the human parties affected by AI decisions, (4) information on human preferences made available to AI, and (5) the objective posed to AI (e.g., representing one of the human individuals interests). Repeated prompts will generate a profile of AI behavior in each treatment.

Experimental Design Details

Not available

Randomization Method

Each AI prompt in each treatment will produce an independent draw on AI behavior under the treatment. Because the underlying AI model is not altered by prior prompts, each treatment-specific sample of AI responses will constitute a random sample of AI behavior under the treatment.

Randomization Unit

Randomization is at an individual level.

Was the treatment clustered?