Modeling Strategic Interactions Between Algorithmic Decision-Makers and Agents with Persistent Improvement

Core Concepts

Agents can strategically improve their qualifications over time to receive favorable decisions from an algorithmic decision-maker, and the decision-maker can design optimal policies to incentivize the largest improvements within the agent population.

Abstract

The paper studies the strategic interactions between a decision-maker who uses an algorithm to make decisions about human agents, and the agents who may exert effort to improve their qualifications in order to receive favorable decisions.
The key highlights are:

The authors propose a novel two-stage Stackelberg game model to capture the delayed and persistent impacts of agents' improvement efforts, in contrast to prior works that assume immediate benefits.

They analyze the conditions under which agents have incentives to exert honest efforts to improve their qualifications, and identify the optimal policy for the decision-maker to incentivize the largest improvements within the agent population.

The authors extend the model to consider the possibility of dishonest behavior, where agents can manipulate the observable assessment outcomes to game the algorithm. They identify conditions under which agents prefer honest improvement over manipulation.

They further propose a forgetting mechanism to examine the case where honest efforts are not sufficient to guarantee persistent improvements, and analyze the impacts on agent behavior and long-term qualifications.

Experiments on real-world data are conducted to evaluate the analytical results.

Stats

The paper presents analytical results and does not contain specific numerical data.

Quotes

"Unlike prior works that assume agents benefit from their efforts immediately, we consider realistic scenarios where the impacts of these efforts are persistent and agents benefit from efforts by making improvements gradually."
"We first develop a dynamic model to characterize persistent improvements and based on this construct a Stackelberg game to model the interplay between agents and the decision-maker."
"We also extend the model to settings where 1) agents may be dishonest and game the algorithm into making favorable but erroneous decisions; 2) honest efforts are forgettable and not sufficient to guarantee persistent improvements."

Key Insights Distilled From

Algorithmic Decision-Making under Agents with Persistent Improvement

by Tian Xie,Xuw... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01807.pdf

Deeper Inquiries

How can the decision-maker estimate the discounting factor and other parameters in practice when they are not directly observable

In practice, estimating the discounting factor and other parameters when they are not directly observable can be challenging but feasible through various approaches:

Experimental Studies: The decision-maker can conduct controlled experiments where agents are incentivized to reveal their preferences and behaviors over time. By observing how agents respond to different scenarios, the decision-maker can infer the discounting factor and other parameters indirectly.

Data Analysis: Utilizing historical data on agent behavior and outcomes, the decision-maker can employ statistical methods to analyze patterns and trends. By fitting models to the data and testing different hypotheses, they can estimate the parameters that govern agent behavior.

Machine Learning Techniques: Machine learning algorithms can be used to predict the parameters based on observed data. By training models on historical interactions and outcomes, the decision-maker can make predictions about the discounting factor and other hidden parameters.

Surveys and Interviews: Directly engaging with agents through surveys and interviews can provide valuable insights into their decision-making processes. By asking targeted questions about their preferences, motivations, and behaviors, the decision-maker can gather information to estimate the parameters.

Behavioral Economics: Drawing on principles from behavioral economics, the decision-maker can design experiments and scenarios that elicit specific responses from agents. By analyzing the results of these experiments, they can infer the underlying parameters that drive agent behavior.

What are the potential negative societal impacts if the decision-maker's optimal policy leads to a large fraction of the agent population choosing to manipulate rather than improve honestly

If the decision-maker's optimal policy leads to a large fraction of the agent population choosing to manipulate rather than improve honestly, several negative societal impacts may arise:

Erosion of Trust: Widespread manipulation can erode trust in the decision-making process, leading to a breakdown in the relationship between agents and the decision-maker. This can have long-term consequences for cooperation and collaboration.

Unfair Outcomes: Manipulation can result in unfair outcomes where undeserving agents are selected over more qualified candidates. This can lead to inefficiencies in resource allocation and hinder overall societal progress.

Diminished Social Welfare: When manipulation prevails, the overall social welfare may decrease as resources are allocated based on false information rather than merit. This can hinder economic growth and societal development.

Reduced Accountability: High levels of manipulation can reduce accountability and transparency in decision-making processes. This can create a culture of dishonesty and undermine the integrity of the system.

Inequality and Discrimination: Manipulation can exacerbate existing inequalities and discrimination by favoring certain groups or individuals unfairly. This can perpetuate social injustices and hinder efforts towards equality.

How can the proposed framework be generalized to settings with multiple rounds of interactions between the decision-maker and agents, where agents can dynamically adjust their strategies over time

To generalize the proposed framework to settings with multiple rounds of interactions where agents can dynamically adjust their strategies over time, the following adaptations can be made:

Dynamic Game Theory: Incorporate dynamic game theory to model the evolving strategies of both the decision-maker and agents over multiple rounds. This allows for the analysis of strategic interactions and optimal decision-making strategies in a changing environment.

Reinforcement Learning: Implement reinforcement learning algorithms to capture the iterative nature of the interactions. Agents can learn and adapt their strategies based on feedback from previous rounds, leading to more sophisticated decision-making processes.

Adaptive Mechanisms: Develop adaptive mechanisms that allow the decision-maker to adjust their policies in response to changing agent behaviors. This flexibility enables the system to respond effectively to new information and evolving strategies.

Long-Term Incentives: Design incentive structures that consider the long-term implications of agent actions. By incentivizing behaviors that lead to sustained improvements rather than short-term gains, the decision-maker can promote honest and beneficial strategies over time.

Simulation and Scenario Analysis: Use simulation and scenario analysis to explore the potential outcomes of different strategies over multiple rounds. By simulating various scenarios and analyzing the results, the decision-maker can identify robust policies that are effective in dynamic environments.