What is he best known for?
The fields of study he is best known for:
- Law
- Microeconomics
- Finance
His primary areas of study are Microeconomics, Mathematical economics, Auction theory, English auction and Revenue equivalence.
His Microeconomics research is mostly focused on the topic Common value auction.
His Mathematical economics research includes themes of Limit price and Oligopoly.
Auction theory is closely attributed to Reverse auction in his work.
His work carried out in the field of Generalized second-price auction brings together such families of science as Forward auction and Combinatorial auction.
His Nash equilibrium research is multidisciplinary, relying on both Symmetric equilibrium and Repeated game.
His most cited work include:
- Bid, ask and transaction prices in a specialist market with heterogeneously informed traders (4345 citations)
- Multitask Principal–Agent Analyses: Incentive Contracts, Asset Ownership, and Job Design (4174 citations)
- Economics, Organization and Management (3511 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Microeconomics, Common value auction, Mathematical economics, Auction theory and Incentive.
The Microeconomics study combines topics in areas such as Rational expectations, Private information retrieval and Operations research.
The study incorporates disciplines such as Bidding, Core and Revenue in addition to Common value auction.
His research combines General equilibrium theory and Mathematical economics.
Paul Milgrom regularly ties together related areas like Industrial organization in his Incentive studies.
His work is dedicated to discovering how Vickrey auction, English auction are connected with Vickrey–Clarke–Groves auction, Proxy bid, Multiunit auction and Unique bid auction and other disciplines.
He most often published in these fields:
- Microeconomics (33.19%)
- Common value auction (28.76%)
- Mathematical economics (20.35%)
What were the highlights of his more recent work (between 2011-2021)?
- Common value auction (28.76%)
- Microeconomics (33.19%)
- Mathematical economics (20.35%)
In recent papers he was focusing on the following fields of study:
Paul Milgrom mainly investigates Common value auction, Microeconomics, Mathematical economics, Incentive and Auction theory.
His Common value auction research includes themes of Value, Advertising and Process.
His work on Vickrey auction as part of general Microeconomics study is frequently linked to Complementarity, therefore connecting diverse disciplines of science.
Paul Milgrom studied Incentive and Industrial organization that intersect with Investment, License and Core.
His Auction theory research is multidisciplinary, incorporating perspectives in Reverse auction and English auction.
His study looks at the intersection of Vickrey–Clarke–Groves auction and topics like Combinatorial auction with Econometrics.
Between 2011 and 2021, his most popular works were:
- Designing Random Allocation Mechanisms: Theory and Applications (193 citations)
- Complementarities, Momentum, and the Evolution of Modern Manufacturing (188 citations)
- Informational Asymmetries, Strategic Behavior, and Industrial Organization (89 citations)
In his most recent research, the most cited papers focused on:
- Law
- Microeconomics
- Finance
Microeconomics, Common value auction, Auction theory, Spectrum auction and Incentive are his primary areas of study.
The concepts of his Microeconomics study are interwoven with issues in Resource allocation and Curriculum.
His Procurement auctions study in the realm of Common value auction interacts with subjects such as Class.
His research in Auction theory tackles topics such as Reverse auction which are related to areas like Vickrey–Clarke–Groves auction, Computer network and Vickrey auction.
His Spectrum auction research incorporates elements of Commerce, Mathematical economics, Outcome, Nash equilibrium and Commission.
His study in Incentive is interdisciplinary in nature, drawing from both Notice, Collusion, Public relations and Agency.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
