In microeconomics and contract theory, the first-order approach is a simplifying assumption used to solve models with a principal-agent problem. [1] It suggests that, instead of following the usual assumption that the agent will take an action that is utility-maximizing, the modeller use a weaker constraint, and looks only for actions which satisfy the first-order conditions of the agent's problem. This makes the model mathematically more tractable (usually resulting in closed-form solutions), but it may not always give a valid solution to the agent's problem. [2]
Historically, [1] the first-order approach was the main tool used to solve the first formal moral hazard models, such as those of Richard Zeckhauser, [3] Michael Spence, [4] and Joseph Stiglitz. [5] Not long after these models were published, James Mirrlees was the first to point out that the approach was not generally valid, and sometimes imposed even stronger necessary conditions than those of the original problem. [2] Following this realization, he [6] and other economists such as Bengt Holmström, [7] William P. Rogerson [2] and Ian Jewitt [8] gave both sufficient conditions for cases where the first-order approach gives a valid solution to the problem, and also different techniques that could be applied to solve general principal-agent models.
In mathematical terms, the first-order approach relaxes the more general incentive compatibility constraint in the principal's problem. The principal decides on an action and proposes a contract to the agent by solving the following program:
where and are the principal's and the agent's expected utilities, respectively. Constraint is usually called the participation constraint (where is the agent's reservation utility), and constraint is the incentive compatibility constraint.
Constraint states that the action that the principal wants the agent to take must be utility-maximizing for the agent – that is, it must be compatible with her incentives. The first-order approach relaxes this constraint with the first-order condition
Equation is oftentimes much simpler and easier to work with than constraint , which justifies the attractiveness of the first-order approach. Nonetheless, it is only a necessary condition, and not equivalent to the more general incentive compatibility constraint.
Mathematical optimization or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.
In economics, a moral hazard is a situation where an economic actor has an incentive to increase its exposure to risk because it does not bear the full costs of that risk. For example, when a corporation is insured, it may take on higher risk knowing that its insurance will pay the associated costs. A moral hazard may occur where the actions of the risk-taking party change to the detriment of the cost-bearing party after a financial transaction has taken place.
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Mechanism design is a branch of economics, social choice theory, and game theory that deals with designing games to implement a given social choice function. Because it starts at the end of the game and then works backwards to find a game that implements it, it is sometimes called reverse game theory.
The principal–agent problem refers to the conflict in interests and priorities that arises when one person or entity takes actions on behalf of another person or entity. The problem worsens when there is a greater discrepancy of interests and information between the principal and agent, as well as when the principal lacks the means to punish the agent. The deviation from the principal's interest by the agent is called "agency costs".
Paul Robert Milgrom is an American economist. He is the Shirley and Leonard Ely Professor of Humanities and Sciences at the Stanford University School of Humanities and Sciences, a position he has held since 1987. He is a professor in the Stanford School of Engineering as well and a Senior Fellow at the Stanford Institute for Economic Research. Milgrom is an expert in game theory, specifically auction theory and pricing strategies. He is the winner of the 2020 Nobel Memorial Prize in Economic Sciences, together with Robert B. Wilson, "for improvements to auction theory and inventions of new auction formats".
A Bellman equation, named after Richard E. Bellman, is a necessary condition for optimality associated with the mathematical optimization method known as dynamic programming. It writes the "value" of a decision problem at a certain point in time in terms of the payoff from some initial choices and the "value" of the remaining decision problem that results from those initial choices. This breaks a dynamic optimization problem into a sequence of simpler subproblems, as Bellman's “principle of optimality" prescribes. The equation applies to algebraic structures with a total ordering; for algebraic structures with a partial ordering, the generic Bellman's equation can be used.
Personnel economics has been defined as "the application of economic and mathematical approaches and econometric and statistical methods to traditional questions in human resources management". It is an area of applied micro labor economics, but there are a few key distinctions. One distinction, not always clearcut, is that studies in personnel economics deal with the personnel management within firms, and thus internal labor markets, while those in labor economics deal with labor markets as such, whether external or internal. In addition, personnel economics deals with issues related to both managerial-supervisory and non-supervisory workers.
In mathematics and economics, the envelope theorem is a major result about the differentiability properties of the value function of a parameterized optimization problem. As we change parameters of the objective, the envelope theorem shows that, in a certain sense, changes in the optimizer of the objective do not contribute to the change in the objective function. The envelope theorem is an important tool for comparative statics of optimization models.
In monotone comparative statics, the single-crossing condition or single-crossing property refers to a condition where the relationship between two or more functions is such that they will only cross once. For example, a mean-preserving spread will result in an altered probability distribution whose cumulative distribution function will intersect with the original's only once.
Screening in economics refers to a strategy of combating adverse selection – one of the potential decision-making complications in cases of asymmetric information – by the agent(s) with less information.
The revelation principle is a fundamental principle in mechanism design. It states that if a social choice function can be implemented by an arbitrary mechanism, then the same function can be implemented by an incentive-compatible-direct-mechanism with the same equilibrium outcome (payoffs).
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The structured support-vector machine is a machine learning algorithm that generalizes the Support-Vector Machine (SVM) classifier. Whereas the SVM classifier supports binary classification, multiclass classification and regression, the structured SVM allows training of a classifier for general structured output labels.
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A borrowing limit is the amount of money that individuals could borrow from other individuals, firms, banks or governments. There are many types of borrowing limits, and a natural borrowing limit is one specific type of borrowing limit among those. When individuals are said to face the natural borrowing limit, it implies they are allowed to borrow up to the sum of all their future incomes. A natural debt limit and a natural borrowing constraint are other ways to refer to the natural borrowing limit.
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In the mathematical subjects of information theory and decision theory, Blackwell's informativeness theorem is an important result related to the ranking of information structures, or experiments. It states that there is an equivalence between three possible rankings of information structures: one based in expected utility, one based in informativeness, and one based in feasibility. This ranking defines a partial order over information structures known as the Blackwell order, or Blackwell's criterion.