Parsimonious Theorising

Trial Information

General Information

Title

RCT ID

AEARCTR-0015867

Initial registration date

April 23, 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

April 30, 2025, 9:06 AM EDT

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

Locations

Country

Italy

Region

Milan

Primary Investigator

Name

Saeid Kazemi

Affiliation

Università Bocconi

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Arnaldo Camuffo

PI Affiliation

Bocconi University

Contact Investigator

PI Name

Diego Jannace

PI Affiliation

Bocconi University

Contact Investigator

PI Name

Alfonso Gambardella

PI Affiliation

Bocconi University

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2025-04-24

End date

2025-04-25

Keywords

Behavior, Firms & Productivity

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

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

Abstract

We apply the “law of parsimony”, also referred to as “Occam’s razor”, to entrepreneurial theories of value. Our key intuition is that decision-makers should build theories of value that are parsimonious, i.e., the simplest contingent on the level of novelty/complexity of the problem at hand. We explore this research question developing a formal model of how and why decision-makers should select the optimal level of complexity of the conceptual causal structure underlying their theories of value. By theory complexity we mean the number of elements, relationships, and layers of articulation it comprises.

This randomized experiment tests the empirical predictions of a formal model predicting that contingent on the level of novelty/entropy of the problem, the theory’s conceptual causal structure has an optimal level of complexity. Additionally, our interventions will allow to test the causal effect of sharper causal reasoning, reduced cognitive constraints, access to broader information sets, and different levels of problem novelty/entropy on the architectural properties of theories.

To test these predictions, we leverage on the randomized control trial registered on this website with registration identifier AEARCTR-0014579. We use the same experimental design and data, with additional variables and measures to investigate our theoretical model. Participants will be tasked with developing theories of value for assigned entrepreneurial challenges that vary in their level of novelty/entropy. Participants will also be exposed to different technological conditions that expand their information set / knowledge base, reduce cognitive costs, and influence the level of causality of their reasoning. By virtue of these manipulations as well as a data collection survey to gather individual characteristics, we aim to observe how the key factors of our formal model affect the complexity of theories, and more specifically, their level of parsimony.

External Link(s)

Registration Citation

Citation

Camuffo, Arnaldo et al. 2025. "Parsimonious Theorising ." AEA RCT Registry. April 30. https://doi.org/10.1257/rct.15867-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Interventions

In this study, we employ two main types of experimental interventions:

Novelty/Entropy Treatment: this intervention exogenously changes the level of novelty/entropy of the target state (high/low problem novelty/entropy)

AI Treatment: This intervention exogenously affects the cognitive capabilities and the size and understanding of the decision maker’s domain. It does so by assigning participants to one of three following experimental conditions:

Human-only – No additional assistance. Participants will be instructed to develop their theories to address the given decision challenge without any additional assistance or tools.

General AI Assistance - Access to an advanced general AI model (OpenAI O3-mini). Participants will have access to a General AI model as a support to aid in brainstorming ideas, answering questions, and refining their theories.

Specialized Agentic AI Assistance – Access to a Specialized Agentic AI System trained on notions of causality and fit (orthogonality of causes). Participants will have access to a specialized agentic AI system based on OpenAI O3-mini, instructed on notions of causality and fit (orthogonality of causes).

We also leave room for exploring the possible interaction effects between these interventions, although they are not the primary focus of our study.

Participants are randomly assigned to one of the experimental conditions by using blocked minimized randomization, due to the different levels of attrition across arms.

Intervention (Hidden)

Interventions

In this study, we employ two main types of experimental interventions:

Novelty/Entropy Treatment: this intervention exogenously changes the level of novelty/entropy of the target state (high/low problem novelty/entropy)

AI Treatment: This intervention exogenously affects the cognitive capabilities and the size and understanding of the decision maker’s domain. It does so by assigning participants to one of three following experimental conditions:

Human-only – No additional assistance. Participants will be instructed to develop their theories to address the given decision challenge without any additional assistance or tools.

General AI Assistance - Access to an advanced general AI model (OpenAI O3-mini). Participants will have access to a General AI model as a support to aid in brainstorming ideas, answering questions, and refining their theories.

Specialized Agentic AI Assistance – Access to a Specialized Agentic AI System trained on notions of causality and fit (orthogonality of causes). Participants will have access to a specialized agentic AI system based on OpenAI O3-mini, instructed on notions of causality and fit (orthogonality of causes).

We also leave room for exploring the possible interaction effects between these interventions, although they are not the primary focus of our study.

Participants are randomly assigned to one of the experimental conditions by using blocked minimized randomization, due to the different levels of attrition across arms.

Intervention Start Date

2025-04-24

Intervention End Date

2025-04-25

Primary Outcomes

Primary Outcomes (end points)

The outcomes that we measure are the following:

Main Outcomes:

Complexity of the theories: Number of nodes, edges, layers, and conditional independence of the theory.

Expected Probability of Success: Participants' estimated likelihood of successfully achieving the target state of the decision problem based on their developed theories.

Expected probability of success of the Theory as evaluated by human experts and/or LLMs

Main Explanatory Variables:

Novelty/entropy of the target state

Domain size and understanding of the decision-makers

Cognitive capabilities of the decision-makers

Covariates

Demographic data about participants:

Age

Gender

Education Level

Education Field

Industry

Job Function

Experience

Managerial Experience

Entrepreneurial Background

Salary

Country of employment

AI Aversion

AI Familiarity

Confidence

Cognitive self-assessment

Knowledge depth

Knowledge breadth

Primary Outcomes (explanation)

The outcomes that we measure are the following:

Main Outcomes:

Complexity of the theories: Number of nodes, edges, layers, and conditional independence of the theory.

Expected Probability of Success: Participants' estimated likelihood of successfully achieving the target state of the decision problem based on their developed theories.

Expected probability of success of the Theory as evaluated by human experts and/or LLMs

Main Explanatory Variables:

Novelty/entropy of the target state

Domain size and understanding of the decision-makers

Cognitive capabilities of the decision-makers

Covariates

Demographic data about participants:

Age

Gender

Education Level

Education Field

Industry

Job Function

Experience

Managerial Experience

Entrepreneurial Background

Salary

Country of employment

AI Aversion

AI Familiarity

Confidence

Cognitive self-assessment

Knowledge depth

Knowledge breadth

Secondary Outcomes

Secondary Outcomes (end points)

As a secondary outcome, we are also interested in exploring the following performance hypothesis:

More parsimonious theories will be evaluated (by human experts and/or LLMs) as having higher expected probability of success.

Secondary Outcomes (explanation)

As a secondary outcome, we are also interested in exploring the following performance hypothesis:

More parsimonious theories will be evaluated (by human experts and/or LLMs) as having higher expected probability of success.

Experimental Design

The design of this randomised controlled trial mimics that of the trial “A Design Centric View of AI Enhanced Decision Making” (Camuffo et al., 2025), registered on the AEA registry website with registration identifier AEARCTR-0014579. Whereas we use the same experimental design, we complement data collection with additional variables to test our theoretical model.

Experimental Design Details

The design of this randomised controlled trial mimics that of the trial “A Design Centric View of AI Enhanced Decision Making” (Camuffo et al., 2025), registered on the AEA registry website with registration identifier AEARCTR-0014579. Whereas we use the same experimental design, we complement data collection with additional variables to test our theoretical model.

Randomization Method

To allocate participants to experimental conditions, we use minimized blocked randomization (batch size = 50) based on three covariates: a) education level and field; b) AI aversion; and c) level of self-confidence. This provides the baseline allocation of participants to the experimental conditions.

To ensure a balanced number of participants in each group and to manage the flow of the experiment, we limit the number of participants who can enter the experimental platform at the same time. This helps prevent an imbalance at the end of the experiment caused by participants who start but do not finish the experiment.

Randomization Unit

Individuals

Was the treatment clustered?

No

Experiment Characteristics

Sample size: planned number of clusters

No clusters

Sample size: planned number of observations

2500 managers from Prolific following the trial “A Design Centric View of AI Enhanced Decision Making” (Camuffo et al., 2025), registered on the AEA registry website with registration identifier AEARCTR-0014579

Sample size (or number of clusters) by treatment arms

208

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Given that this experiment leverages the trial “A Design Centric View of AI Enhanced Decision Making” (Camuffo et al., 2025), registered on AEA registry website with registration identifier AEARCTR-0014579, we expect, with a sample size of 208 observations per arm, to detect a minimum effect size of 0.24 (standardized), calculated using G*Power 3.1.9.7, command t-tests, Means: Difference between two independent means (two groups). In the power calculations we also assume standard type I and type II errors, α err prob = 0.05, and Power (1-β err prob) = 0.80, with an allocation ratio of 1.

Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name

A Design Centric View of AI Enhanced Decision Making

IRB Approval Date

2024-10-22

IRB Approval Number

RA000826

Analysis Plan

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Parsimonious Theorising

Pre-Trial

General Information

Locations

Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Registration Citation

Interventions

Primary Outcomes

Secondary Outcomes

Experimental Design

Experiment Characteristics

Institutional Review Boards (IRBs)

Post-Trial

Study Withdrawal

Intervention

Data Publication

Program Files

Relevant Paper(s)

Reports & Other Materials