Add to ORKGA "statistician" takes an action on behalf of an agent, based on the agent's self-reported personal data and a sample involving other people. The action that he takes is an estimated function of the agent's report. The estimation procedure involves model selection. We ask the following question: Is truth-telling optimal for the agent given the statistician's procedure? We analyze this question in the context of a simple example that highlights the role of model selection. We suggest that our simple exercise may have implications for the broader issue of human interaction with "machine learning" algorithms
When subjected to automated decision-making, decision subjects may strategically modify their observ...
People reason about uncertainty with deliberately incomplete models, including only the most relevan...
Model selection is a necessary step in unsupervised machine learning. Despite numerous criteria and ...
A "statistician" takes an action on behalf of an agent, based on the agent's self-reported personal ...
Conventional wisdom usually suggests that agents should use all the data they have to make the best ...
We initiate the study of incentives in a general machine learning framework. We focus on a game theo...
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...
We derive some decision rules to select best predictive regression models in a credibility context, ...
The purpose of this research is to investigate the possibility of using aspects of model selection t...
Different agents need to make a prediction. They observe identical data, but have different models: ...
In survey methodology, inverse probability weighted (Horvitz-Thompson) estimation has become an indi...
Adaptive generation of hypotheses is among the main culprits of the lack of replicability in science...
Economists and psychologists have recently been developing new theories of decision making under unc...
Conventional statistical inference requires that a model of how the data were generated be known bef...
AbstractWe initiate the study of incentives in a general machine learning framework. We focus on a g...
When subjected to automated decision-making, decision subjects may strategically modify their observ...
People reason about uncertainty with deliberately incomplete models, including only the most relevan...
Model selection is a necessary step in unsupervised machine learning. Despite numerous criteria and ...
A "statistician" takes an action on behalf of an agent, based on the agent's self-reported personal ...
Conventional wisdom usually suggests that agents should use all the data they have to make the best ...
We initiate the study of incentives in a general machine learning framework. We focus on a game theo...
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...
We derive some decision rules to select best predictive regression models in a credibility context, ...
The purpose of this research is to investigate the possibility of using aspects of model selection t...
Different agents need to make a prediction. They observe identical data, but have different models: ...
In survey methodology, inverse probability weighted (Horvitz-Thompson) estimation has become an indi...
Adaptive generation of hypotheses is among the main culprits of the lack of replicability in science...
Economists and psychologists have recently been developing new theories of decision making under unc...
Conventional statistical inference requires that a model of how the data were generated be known bef...
AbstractWe initiate the study of incentives in a general machine learning framework. We focus on a g...
When subjected to automated decision-making, decision subjects may strategically modify their observ...
People reason about uncertainty with deliberately incomplete models, including only the most relevan...
Model selection is a necessary step in unsupervised machine learning. Despite numerous criteria and ...