Experts' Strategic Adoption of Algorithmic Decision Aids in Credence Goods Markets with Heterogeneous Diagnostic Abilities

Core Concepts

High-ability experts may strategically forego algorithmic decision aids in order to signal their superior diagnostic abilities to consumers and escape a pooling equilibrium with low-ability experts.

Abstract

The content analyzes how technological shocks, specifically the introduction of algorithmic decision aids, affect expert behavior and market efficiency in credence goods markets where experts have heterogeneous and obfuscated diagnostic abilities.
The key findings are:

In the initial phase without investments, experts do not generally maximize consumer income by choosing the optimal price vector. Instead, they often choose the price vector that splits the gains of trade most equally if they behave honestly, leading to efficiency losses.

In the second phase, when experts can invest to improve their diagnostic precision, low-ability experts always invest in the Skill treatment to increase their abilities. High-ability experts, however, are significantly less likely than low-ability experts to invest in the Algorithm treatment, as they can strategically forego the algorithmic decision aid to signal their superior abilities and escape the pooling equilibrium.

Overall, the market is relatively efficient, with high consumer participation rates. However, experts generally under-invest in diagnostic improvements, potentially because consumers do not sufficiently reward beneficial investment patterns.

Investments tend to shift expert price menus towards more self-serving options that reward fraudulent undertreatment, suggesting a potential downside of technological progress in credence goods markets.

Stats

"Experts always choose Pm irrespective of type."
"There is no difference between high-ability expert and low-ability expert behavior."
"Consumers always enter the market."
"Low-ability experts in Skill always invest to improve their diagnostic ability."
"Less high-ability experts than low-ability experts in Algorithm invest to rent the algorithmic decision aid."

Quotes

"High-ability experts may be incentivized to forego the decision aid in order to escape a pooling equilibrium by differentiating themselves from low-ability experts."
"Relying on an algorithmic decision system precludes the expert from sending a competence signal, and may well indicate the opposite."

Key Insights Distilled From

Technological Shocks and Algorithmic Decision Aids in Credence Goods Markets

by Alexander Er... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2401.17929.pdf

Deeper Inquiries

How would the results change if consumers had the ability to directly observe expert abilities, rather than having them obfuscated

If consumers had the ability to directly observe expert abilities instead of having them obfuscated, the results of the experiment would likely change significantly. With transparent expert abilities, consumers would be able to accurately assess the expertise of each expert, leading to more informed decision-making. This transparency would eliminate the need for experts to signal their abilities through their actions, as consumers could simply choose the most qualified expert based on their observable skills.
In this scenario, high-ability experts would have a clear advantage over low-ability experts, as consumers would preferentially choose experts with higher diagnostic precision. This would likely result in high-ability experts attracting more clients and earning higher profits compared to low-ability experts. The market would likely become more efficient, with consumers receiving better quality treatments and experts being rewarded based on their actual abilities rather than their signaling strategies.

What other strategic considerations might experts have when deciding whether to adopt algorithmic decision aids, beyond signaling their abilities

Beyond signaling their abilities, experts may have other strategic considerations when deciding whether to adopt algorithmic decision aids. One important consideration is the cost-benefit analysis of investing in the decision aid versus the potential benefits it can provide. Experts may weigh the upfront costs of acquiring and implementing the technology against the potential long-term gains in efficiency, accuracy, and client satisfaction.
Additionally, experts may consider the impact of using algorithmic decision aids on their reputation and perceived expertise. Relying on technology to assist in decision-making may affect how experts are perceived by consumers, potentially influencing their trust and confidence in the expert's judgment. Experts may also consider the competitive landscape and how adopting decision aids may give them a competitive edge or differentiate them from other experts in the market.
Furthermore, experts may take into account the ethical implications of using algorithmic decision aids, such as concerns about bias in the algorithms, the potential for errors or malfunctions, and the impact on the doctor-patient relationship. Balancing these strategic considerations with the desire to provide high-quality care and maintain a positive reputation is crucial for experts when deciding whether to adopt algorithmic decision aids.

How might the dynamics change if experts could choose to invest in improving their own diagnostic abilities versus renting an algorithmic decision aid, rather than having to choose between the two

If experts had the option to choose between investing in improving their own diagnostic abilities and renting an algorithmic decision aid, rather than being limited to one choice, the dynamics of the market would likely change. This additional choice would introduce a new layer of complexity to experts' decision-making processes and could impact their strategies for differentiation and competition.
Experts would need to carefully evaluate the trade-offs between investing in their own skills and utilizing algorithmic decision aids. Investing in their own abilities may provide long-term benefits in terms of expertise and reputation, while renting decision aids could offer immediate improvements in diagnostic precision and efficiency. Experts would need to consider factors such as the cost of investment, the expected return on investment, and the potential impact on client outcomes and satisfaction.
The choice between investing in personal abilities and renting decision aids could also influence consumer perceptions and preferences. Consumers may have varying preferences for experts who rely on technology versus those who rely on their own skills, which could impact experts' client base and market positioning. Experts would need to strategically align their choices with consumer expectations and market demands to maximize their success and competitiveness in the market.