Canons of Just Taxation: Efficiency and Fairness in an Economy with a Public Good

1986 ◽  
Vol 14 (4) ◽  
pp. 448-465 ◽  
Author(s):  
Dennis Sullivan ◽  
Harris Schlesinger
Keyword(s):  
Public Good ◽  
Pareto Optimality ◽  
Individual Rationality ◽  
Pareto Optimal ◽  
Optimal Point ◽  
Progressive Taxation ◽  
Lindahl Equilibrium ◽  
Close Relationship ◽  
Rawlsian Justice ◽  
Pareto Optimal Point

This article analyzes the relationships among three canons of “just” taxation: Pareto optimality, individual rationality, and fairness (nonenvy). Using a helpful device called a Kolm triangle, the analysis shows that the fair and Pareto optimal point need not be individually rational, that it will involve progressive taxation, and that it bears no particular relationship to Lindahl equilibrium, but a rather close relationship to Rawlsian justice.

scholarly journals On Secure Exact-Repair Regenerating Codes With a Single Pareto Optimal Point

2020 ◽  
Vol 66 (1) ◽  
pp. 176-201 ◽  
Author(s):  
Fangwei Ye ◽  
Shiqiu Liu ◽  
Kenneth W. Shum ◽  
Raymond W. Yeung
Keyword(s):  
Pareto Optimal ◽  
Optimal Point ◽  
Regenerating Codes ◽  
Exact Repair ◽  
Pareto Optimal Point

scholarly journals The Core of a Reinsurance Market

1981 ◽  
Vol 12 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Bernard Baton ◽  
Jean Lemaire
Keyword(s):  
Pareto Optimality ◽  
Individual Rationality ◽  
Pareto Optimal ◽  
Collective Rationality ◽  
Important Special Case ◽  
The Core ◽  
Premium Calculation ◽  
The Individual ◽  
Special Case ◽  
Reinsurance Market

In a series of celebrated papers, K. Borch characterized the set of the Pareto-optimal risk exchange treaties in a reinsurance market. However, the Pareto-optimality and the individual rationality conditions, considered by Borch, do not preclude the possibility that a coalition of companies might be better off by seceding from the whole group. In this paper, we introduce this collective rationality condition and characterize the core of this game without transferable utilities in the important special case of exponential utilities. The mathematical conditions we obtain can be interpreted in terms of insurance premiums, calculated by means of the zero-utility premium calculation principle. We then show that the core is always non-void and conclude by an example.

scholarly journals Radar Beam Scheduling Using Pareto Optimal Point to Optimize Dual Cost Function

2019 ◽  
Vol 30 (8) ◽  
pp. 677-687
Author(s):  
Seung-Hyeon Jin ◽  
Nam-Hoon Jeong ◽  
Jae-Ho Choi ◽  
Seong-Hyeon Lee ◽  
Cheol-Ho Kim ◽  
...  
Keyword(s):  
Cost Function ◽  
Pareto Optimal ◽  
Optimal Point ◽  
Radar Beam ◽  
Pareto Optimal Point

Pareto domain: an invaluable source of process information

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Geraldine Cáceres Sepúlveda ◽  
Silvia Ochoa ◽  
Jules Thibault
Keyword(s):  
Case Studies ◽  
Optimal Solution ◽  
Pareto Optimal ◽  
Process Variables ◽  
Optimal Point ◽  
Decision Variables ◽  
Visualization Tools ◽  
Pareto Domain ◽  
And Performance ◽  
Typical Process

AbstractDue to the highly competitive market and increasingly stringent environmental regulations, it is paramount to operate chemical processes at their optimal point. In a typical process, there are usually many process variables (decision variables) that need to be selected in order to achieve a set of optimal objectives for which the process will be considered to operate optimally. Because some of the objectives are often contradictory, Multi-objective optimization (MOO) can be used to find a suitable trade-off among all objectives that will satisfy the decision maker. The first step is to circumscribe a well-defined Pareto domain, corresponding to the portion of the solution domain comprised of a large number of non-dominated solutions. The second step is to rank all Pareto-optimal solutions based on some preferences of an expert of the process, this step being performed using visualization tools and/or a ranking algorithm. The last step is to implement the best solution to operate the process optimally. In this paper, after reviewing the main methods to solve MOO problems and to select the best Pareto-optimal solution, four simple MOO problems will be solved to clearly demonstrate the wealth of information on a given process that can be obtained from the MOO instead of a single aggregate objective. The four optimization case studies are the design of a PI controller, an SO2 to SO3 reactor, a distillation column and an acrolein reactor. Results of these optimization case studies show the benefit of generating and using the Pareto domain to gain a deeper understanding of the underlying relationships between the various process variables and performance objectives.

Some Sufficiency Conditions for Pareto-Optimal Control

1973 ◽  
Vol 95 (4) ◽  
pp. 356-361 ◽  
Author(s):  
G. Leitmann ◽  
W. Schmitendorf
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Pareto Optimality ◽  
Optimal Control Theory ◽  
Cooperative Games ◽  
Sufficient Conditions ◽  
Pareto Optimal ◽  
Vector Valued ◽  
Sufficiency Conditions

We consider the optimal control problem with vector-valued criterion (including cooperative games) and seek Pareto-optimal (noninferior) solutions. Scalarization results, together with modified sufficiency theorems from optimal control theory, are used to deduce sufficient conditions for Pareto-optimality. The utilization of these conditions is illustrated by various examples.

Public Goods and the Property Tax: A Theoretical Analysis

1976 ◽  
Vol 4 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Clarence C. Morrison
Keyword(s):  
Theoretical Analysis ◽  
Public Goods ◽  
Public Good ◽  
Property Tax ◽  
Pareto Optimal ◽  
Public Provision

In this paper a property tax model is constructed in which the proceeds of the property tax are used to provide a public good. Generally speaking, it is argued that public provision of public goods will not be Pareto optimal in that public provision will result in the oversupply of public goods.

The Skewboid Method: A Simple and Effective Approach to Pareto Relaxation and Filtering

2012 ◽  
Author(s):  
Matthew I. Campbell
Keyword(s):  
Pareto Optimality ◽  
Population Based ◽  
Pareto Optimal ◽  
Large Set ◽  
Number Of Solutions ◽  
Target Number ◽  
Relaxation Methods ◽  
Pareto Optimal Set ◽  
Optimal Set ◽  
Adjustment Parameter

The concept of Pareto optimality is the default method for pruning a large set of candidate solutions in a multi-objective problem to a manageable, balanced, and rational set of solutions. While the Pareto optimality approach is simple and sound, it may select too many or too few solutions for the decision-maker’s needs or the needs of optimization process (e.g. the number of survivors selected in a population-based optimization). This inability to achieve a target number of solutions to keep has caused a number of researchers to devise methods to either remove some of the non-dominated solutions via Pareto filtering or to retain some dominated solutions via Pareto relaxation. Both filtering and relaxation methods tend to introduce many new adjustment parameters that a decision-maker (DM) must specify. In the presented Skewboid method, only a single parameter is defined for both relaxing the Pareto optimality condition (values between −1 and 0) and filtering more solutions from the Pareto optimal set (values between 0 and 1). This parameter can be correlated with a desired number of solutions so that this number of solutions is input instead of an unintuitive adjustment parameter. A mathematically sound derivation of the Skewboid method is presented followed by illustrative examples of its use. The paper concludes with a discussion of the method in comparison to similar methods in the literature.

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