Kfir Eliaz
Biographic Data
| ID | 5731984 |
|---|---|
| NAME | Kfir Eliaz |
| GIVEN NAMES | Kfir |
| FAMILY NAME | Eliaz |
| SIGNATURE | ELIAZ K |
| AFFILIATIONS | Brown University |
| ORCID | 0000-0001-8988-0726 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 9 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2011 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Should Humans Lie to Machines? The Incentive Compatibility of Lasso and GLM Structured Sparsity Estimators
We consider situations where a user feeds her attributes to a machine learning method that tries to predict her best option based on a random sample of other users. The predictor is incentive-compatible if the user has no incentive to misreport her covariates. Focusing on the popular Lasso estimation technique, we borrow tools from high-dimensional statistics to characterize sufficient conditions that ensure that Lasso is incentive compatible in …
Competing for Consumer Inattention
Consumers purchase multiple types of goods but may be able to examine only a limited number of markets for the best price. We propose a simple model that captures these features, conveying new insights. A firm’s price can deflect or draw attention to its market, and consequently, limited attention introduces a new dimension of cross-market competition. We characterize the equilibrium and show that having partially attentive consumers improves con…
A Simple Model of Search Engine Pricing
We present a simple model of how a monopolistic search engine optimally determines the average relevance of firms in its search pool. In our model, there is a continuum of consumers, who use the search engine’s pool, and there is a continuum of firms, whose entry to the pool is restricted by a price‐per‐click set by the search engine. We show that a monopolistic search engine may have an incentive to set a relatively low price‐per‐click that enco…
A Simple Model of Search Engine Pricing
We present a simple model of how a monopolistic search engine optimally determines the average relevance of firms in its search pool. In our model, there is a continuum of consumers, who use the search engine’s pool, and there is a continuum of firms, whose entry to the pool is restricted by a price‐per‐click set by the search engine. We show that a monopolistic search engine may have an incentive to set a relatively low price‐per‐click that enco…
Competing for Consumer Inattention
Consumers purchase multiple types of goods but may be able to examine only a limited number of markets for the best price. We propose a simple model that captures these features, conveying new insights. A firm’s price can deflect or draw attention to its market, and consequently, limited attention introduces a new dimension of cross-market competition. We characterize the equilibrium and show that having partially attentive consumers improves con…
A Simple Model of Search Engine Pricing
We present a simple model of how a monopolistic search engine optimally determines the average relevance of firms in its search pool. In our model, there is a continuum of consumers, who use the search engine’s pool, and there is a continuum of firms, whose entry to the pool is restricted by a price‐per‐click set by the search engine. We show that a monopolistic search engine may have an incentive to set a relatively low price‐per‐click that enco…
Competing for Consumer Inattention
Consumers purchase multiple types of goods but may be able to examine only a limited number of markets for the best price. We propose a simple model that captures these features, conveying new insights. A firm’s price can deflect or draw attention to its market, and consequently, limited attention introduces a new dimension of cross-market competition. We characterize the equilibrium and show that having partially attentive consumers improves con…
Should Humans Lie to Machines? The Incentive Compatibility of Lasso and GLM Structured Sparsity Estimators
We consider situations where a user feeds her attributes to a machine learning method that tries to predict her best option based on a random sample of other users. The predictor is incentive-compatible if the user has no incentive to misreport her covariates. Focusing on the popular Lasso estimation technique, we borrow tools from high-dimensional statistics to characterize sufficient conditions that ensure that Lasso is incentive compatible in …
Computer Science (2 works) · Consumer Market Behavior and Pricing (2 works) · Economics (2 works) · Advanced Bandit Algorithms Research (1 works) · Advanced Causal Inference Techniques (1 works) · Advertising (1 works) · Artificial Intelligence (1 works) · Business (1 works) · Covariate (1 works) · Digital Platforms and Economics (1 works)