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We introduce a deep learning classification (DLC) method for analyzing equilibrium in discrete-continuos choice dynamic models. As an illustration, we apply the DLC method to solve a version of Krusell and Smith's (1998) heterogeneous-agent model with incomplete markets, borrowing constraint and indivisible labor choice. The novel feature of our analysis is that we construct discontinuous decision functions that tell us when the agent switches from one employment state to another, conditional on the economy's state. We use deep learning not only to characterize the discrete indivisible choice but also to perform model reduction and to deal with multicollinearity. Our TensorFlow-based implementation of DLC is tractable in models with thousands of state variables.

We provide an intuitive interpretation for critical growth of an atomic-scale plastic flow unit in metallic glasses through energy conversion approach. An explicit expressions for the critical stress upon which the plastic flow unit can grow spontaneously is obtained. The growth of the atomic units is an competition process between the intrinsic configuration entropy change and the constraint effect of glassy matrix on the inelastic deformation of plastic flow unit. Our analysis shows that at the yielding point, the activation of a plastic flow unit should take the easiest path in the potential energy landscape. In this case, the change of intrinsic configuration entropy and the elastic constraint effect contribute equally to the activation energy barrier for the plastic flow unit. Our results provide quantificational insights on the microscopic origin of the plastic yielding of glassy solids.

This work presents a disciplined convex programming framework for Kelly criterion in portfolio optimization based on exponential cone programming. This framework allows us to incorporate mean logarithmic return in problems like maximize mean logarithmic return subject to a risk constraint, maximize risk adjusted logarithmic return and constraints on mean logarithmic return. The advantage of this framework is that gives us an exact solution that considers all moments instead of approximate solution that only considers first and second moments. Finally, we run some numerical examples using Python, Riskfolio-Lib package and MOSEK solver, comparing asset allocations obtained using arithmetic return, approximate and exact logarithmic return.

We propose a new optimal consumption model in which the degree of addictiveness of habit formation is directly controlled through a constraint on admissible consumption. In particular, we assume that the individual is unwilling to consume at a rate below a certain proportion 0

This paper studies an optimal reinsurance problem of Pareto-optimality when the contract is subject to default of the reinsurer. We assume that the reinsurer can invest a share of its wealth in a risky asset and default occurs when the reinsurer's end-of-period wealth is insufficient to cover the indemnity. We show that without the solvency regulation, the optimal indemnity function is of excess-of-loss form, regardless of the investment decision. We model solvency regulation as a constraint on the probability of default. Under solvency regulation, by assuming the investment decision remains the same as in the unconstrained solution, the optimal indemnity function is derived element-wisely. Partial results are given when the indemnity function and investment decision are jointly constrained by the solvency regulation. Numerical examples are provided to illustrate the implications of our results and the sensitivity of the solutions to the model parameters.

When applying to schools, students often submit applications to distinct school systems that operate independently, which leads to waste and distortions of stability due to miscoordination. To alleviate this issue, Manjunath and Turhan (2016) introduce the Iterative Deferred Acceptance mechanism (IDA); however, this mechanism is not strategy-proof. We design an experiment to compare the performance of this mechanism under parallel markets (DecDA2) to the classic Deferred Acceptance mechanism with both divided (DecDA) and unified markets (DA). Consistent with the theory, we find that both stability and efficiency are highest under DA, intermediate under DecDA2, and lowest under DecDA. We observe that some subjects use strategic reporting when predicted, leading to improved efficiency for all participants of the market. Our findings cast doubt on whether strategy-proofness should be perceived as a universal constraint to market mechanisms.

Biosemiotics deal with the study of signs and meanings in living entities. Constructivism considers human knowledge as internally constructed by sense making rather than passively reflecting a pre-existing reality. Consequently, a constructivist perspective on biosemiotics leads to look at an internal active construction of meaning in living entities from basic life to humans. That subject is addressed with an existing tool: the Meaning Generator System (MGS) which is a system submitted to an internal constraint related to the nature of the agent containing it (biological or artificial). Simple organisms generate meanings to satisfy a “stay alive” constraint. More complex living entities manage meaningful representations with more elaborated constraints. The generated meanings are used by the agents to implement actions aimed at satisfying the constraints. The actions can be physical, biological or mental and take place in the agent or in its environment. The case of human agency is introduced with meaningful representations that may have allowed our ancestors to become self-conscious by representing themselves as existing entities. This paper proposes to use the MGS as a thread to address the above items linking biosemiotics to constructivism with relations to normativity, agency and autonomy. Possible continuations are introduced.

A chance-constrained binary program (CCBP) is a general optimization problem over binary decision variables restricted by a chance constraint, which ensures that a constraint with uncertain coefficients can be violated only up to a given probability threshold. Despite its wide applications, the CCBP is challenging to solve due to its combinatorial nature and the involvement of its chance constraint. The existing solution methods for the CCBP with tractability guarantees are mainly extended from the methods proposed for problems with continuous decision variables and exploit the parametric information, such as mean and variance, of the uncertain coefficients. In this paper, we propose a general robust optimization framework for the development of solution methods with tractability guarantees for the CCBP, and then follow this framework to develop new solution methods for the CCBP. Unlike the existing solution methods, we exploit the binary characteristic of the decision variables and the non-parametric information about the density function of the uncertain coefficients to devise novel upper bounds on the violation probability of the chance constraint. Based on these upper bounds, we then derive two new robust optimization models to approximate the CCBP. Both models can be decomposed into binary programs, and one of them can be decomposed into nominal problems. Computational results show that our newly proposed solution methods produce significantly better solutions to the CCBP compared with existing methods.

The special, dual nature of property as both a consumption and an investment goodmakes it salient for portfolio choice. In fact, the theoretical literature predicts a con-straint imposed by property on investment and the empirical literature has broughtevidence that this constraint, in some form, exists, but neglecting to investigate its het-erogeneity and to differentiate between owner-occupied and investment property. Withreference to the predictions of a stochastic control model, we turn to the Wealth and As-sets Survey panel for the UK, which allows to break down in detail households’ port-folios, to show empirically how the relationship between property and stockholdingsdepends on the value of property relative to the size of the entire portfolio. While onaverage, an increase in the share of property in the total portfolio is estimated to cor-respond to a slight decrease or to no change in the share of stocks in liquid assets, thisnexus potentially goes from positive to negative depending on the weight of propertyin the portfolio. Consistent with the prediction that only consumption-relevant prop-erty places a constraint on portfolio choice, the relationship can be identified robustlyfor owner-occupied property only.

This article proposes an artificial data generating algorithm that is simple and easy to customize. The fundamental concept is to perform random permutation of Monte Carlo generated random numbers which conform to the unconditional probability distribution of the original real time series. Similar to constraint surrogate methods, random permutations are only accepted if a given objective function is minimized. The objective function is selected in order to describe the most important features of the stochastic process. The algorithm is demonstrated by producing simulated log-returns of the S&P 500 stock index.

On December 15, 1976, a just-promulgated corporate law introduced, among other innovations, a minimum required dividend distribution on annual basis in Brazil. As a conceptual reference, this article refers to the so-called residual theory of dividend policy. According to this theory, dividends are determined from what is left after optimal investment and financing decisions are made by the firm. The introduction of a minimum required dividend represented the imposition of a constraint on that behavior by the firms. The hypothesis that this constraint affected the firms’ investment behavior is tested herein, and this is confirmed with the use of an interaction term with dividend payments. A “difference-in-differences” approach is adopted, with data for 233 publicly owned firms in the 1973-1980 period.

We introduce an approach to sensitivity analysis of quantitative risk models, for the purpose of identifying the most influential inputs. The proposed approach relies on a change of measure derived by minimising the $\chi^2$-divergence, subject to a constraint (`stress') on the expectation of a chosen random variable. We obtain an explicit solution of this optimisation problem in a finite space, consistent with the use of simulation models in risk management. Subsequently, we introduce metrics that allow for a coherent assessment of reverse (i.e. stressing the output and monitoring inputs) and forward (i.e. stressing the inputs and monitoring the output) sensitivities. The proposed approach is easily applicable in practice, as it only requires a single set of simulated input/output scenarios. This is demonstrated by application on a simple insurance portfolio. Furthermore, via a simulation study, we compare the sampling performance of sensitivity metrics based on the $\chi^2$- and the Kullback-Leibler divergence, indicating that the former can be evaluated with lower sampling error.

Although assortment optimization has been extensively studied in the literature, there is lack of studies on exploring the impact of advertising on the output of assortment planning. In this article, we fill this gap by considering a novel joint advertising budget strategy and assortment optimization problem, where the platforms must jointly decide (1) the optimal advertising effort allocated to each product and (2) an assortment of products displayed to consumers. We assume that the purchase behavior of a consumer is governed by the classic multinomial logit choice model, where each product is associated with a preference weight and the purchase probability of a product in an assortment is proportional to its preference weight. To capture the advertising effect in the context of assortment planning, we assume that one can increase the preference weight of a product through advertising that product, and the degree of improvement is decided by a response function in terms of the amount of advertising efforts allocated to that product. Given a budget constraint on advertising, our objective is to find a solution, which is composed of an advertising strategy and an assortment of products, to maximize the expected revenue. We analyze the structural properties of this problem and derive effective solutions under different settings. If there is no capacity constraint on the number of products displayed to consumers, we show that the revenue-ordered assortments still maintain optimality, and we leverage this result to derive an optimal solution. For the capacity constrained case, it is hard to solve the optimization problem directly, we overcome this challenge by proposing an efficient iterative scheme to find the optimal solution.

Instead of using the classical block-shaped market depth to build the optimal execution model, this work studies the constrained optimal execution problem in a limit order book (LOB) market with a power-shaped market depth. Different from the linear price impact derived from the framework of block-shaped market depth, the price impact generated from the framework of our power-shaped market depth becomes a nonlinear function, which is consistent with the empirical studies. We also consider a class of state-dependent upper and lower bound constraints on trading strategies, which includes non-negative constraint (or non-short selling constraint) as its special case. Even though both the power-shaped market depth and trading strategy constraints make it hardly to solve such an optimal execution problem analytically, we still develop some significant properties on the optimal execution policy and optimal execution cost of our model. From some illustrative examples, we find that the optimal execution policy derived from our model is quite different from the one generated from the model with the block-shaped market depth when the market exhibits finite resiliency. However, when the market resilience is infinite, the obtained result becomes different, i.e., the optimal execution policies derived from these two kind of models are equivalent. For a special model with the stochastic block-shaped market depth and infinite market resilience, we successfully derive the analytical solution for such an optimal execution problem by utilizing the state separation property induced from its structure. The revealed optimal execution strategy is a piece-wise affine function with respect to the current remaining position, which can be computed off-line efficiently by solving two coupled equations. Finally, due to its explicit solution, we utilize this optimal execution model to demonstrate that the model admits no price magnification opportunity for the two-sided trading strategy.

In a multi-agent contracting setting, agents' performances are related due to both correlating noise and effort spillover. We study the optimal linear contract in which the agents are risk averse. Putting compensation weights on peers' performances helps to filter out common noise, which relaxes the participation constraint (hedge effect). However, it may also discourage the incentive to exert effort due to effort spillover, which therefore tightens the incentive compatibility constraint (incentive effect). We show that optimal compensation sensitivity is determined by the dot product between pure hedge vector and spillover vector. Besides, we propose an index called informativeness along the spillover direction to capture the balance between hedge and incentive effects. As a result, both the induced effort and generated profit in equilibrium are positively related with this index. Finally, we demonstrate the disparity between spillover depth (measuring the strength of spillover effect) and spillover width (summarizing the density of spillover structure).

We study a target-oriented, multi-period location-transportation problem where customer demands are uncertain and transportation decisions are adaptative. This problem is meant to determine the facility locations, production quantities, capacities, and shipment quantities, with the objective of achieving the desired profit and fill rate to the full extend when reaching both is impossible. To realize that objective, we propose a target-oriented framework for location transportation problem, where service-level measure is constructed to guarantee desired fill rate, and a hard constraint on profit is imposed to ensure decent profit. This framework gets rid of the issues arising from estimating the weights of different objectives in a multi-objective optimization framework. Additionally, to incorporate the characteristics of a multi-period decision-making process, an affine decision rule is introduced, which not only ensures that the transportation decisions of each period can adapt to realized demands wisely, but also prevents the high complexity of the model due to the uncertainty and adaptation. That is to say, to tackle the intractability, we reformulate the robust counterpart into a conservative approximation which is a mixed-integer quadratic programming, and then effective solutions can be produced with Benders Decomposition algorithm. Finally, the performance of the target-oriented framework is assessed through computational experiments based on realistic instances.

Price discrimination, which refers to the strategy of setting different prices for different customer groups, has been widely used in online retailing. Although it helps boost the collected revenue for online retailers, it might create serious concern in fairness, which even violates the regulation and law. This paper studies the problem of dynamic discriminatory pricing under fairness constraints. In particular, we consider a finite selling horizon of length $T$ for a single product with two groups of customers. Each group of customers has its unknown demand function that needs to be learned. For each selling period, the seller determines the price for each group and observes their purchase behavior. While existing literature mainly focuses on maximizing revenue, ensuring fairness among different customers has not been fully explored in the dynamic pricing literature. In this work, we adopt the fairness notion from (Cohen et al. 2021a). For price fairness, we propose an optimal dynamic pricing policy in terms of regret, which enforces the strict price fairness constraint. In contrast to the standard $\sqrt{T}$-type regret in online learning, we show that the optimal regret in our case is $\tilde{\Theta}(T^{4/5})$. We further extend our algorithm to a more general notion of fairness, which includes demand fairness as a special case. To handle this general class, we propose a soft fairness constraint and develop the dynamic pricing policy that achieves $\tilde{O}(T^{4/5})$ regret., Comment: 33pages, 6 figures

We develop a formal model of the internal game between the leader and the factions of a party, to study the effect of party leadership on electoral success. Factions are of interest or of principle. The probability of winning an election is increasing in the leader's charisma, but also in party unity and coherence and in the factions' total contributions to party work and electoral efforts. To push factions to contribute, the leader offers both types of factions their favorite rewards in exchange for their contributions. We show that party unity and factions' total contributions are not necessarily increasing in the leader's charisma and ideological proximity to factions. Further, we show that factions of interest constraint the party's electoral strategy less than factions of principle. In particular, factions of interest always contribute more than factions of principle, are less of an obstacle towards achieving party unity, and offer the party more freedom in its choice of the ideological location and charisma of the party leader.

We study information design settings where the designer controls information about a state, and there are multiple agents interacting in a game who are privately informed about their types. Each agent’s utility depends on all agents’ types and actions, as well as (linearly) on the state. To optimally screen the agents, the designer first asks agents to report their types and then sends a private action recommendation to each agent whose distribution depends on all reported types and the state. We show that there always exists an optimal mechanism which is laminar partitional. Such a mechanism partitions the state space for each type profile and recommends the same action profile for states that belong to the same partition element. Furthermore, the convex hulls of any two partition elements are such that either one contains the other or they have an empty intersection. In the single-agent case, each state is either perfectly revealed or lies in an interval in which the number of different signal realizations is at most the number of different types of the agent plus two. A similar result is established for the multi-agent case. We also highlight the value of screening: without screening the best achievable payoff could be as low as one over the number of types fraction of the optimal payoff. Along the way, we shed light on the solutions of optimization problems over distributions subject to a mean-preserving contraction constraint and additional side constraints, which might be of independent interest.

The paper develops a speech act model of linguistic communication with a public derivability constraint on asserted content that applies both to individual and collective speaker-hearers. The model employs concepts from contemporary philosophy of language that allows us to distinguish ambiguity, vagueness, semantic incompleteness, and semantic under-specificity from one another and to apply standard legal and non-legal practices to resolve the resulting interpretive issues. The resulting enrichment of originalist resources allow us to replace original intentions, original expected applications, and original public linguistic meaning in context with original asserted content as that to which originalists must ultimately be faithful. The importance of this move is illustrated by the interpretations of well known statutory and constitutional provisions it makes possible.

This paper studies a non-concave optimization problem under a Value-at-Risk (VaR) or an Expected Shortfall (ES) constraint. The non-concavity of the problem stems from the non-linear payoff structure of the optimizing investor. We obtain the closed-form optimal wealth with an ES constraint as well as with a VaR constraint respectively, and explicitly calculate the optimal trading strategy for constant relative risk aversion (CRRA) utility functions. In our non-concave optimization problem, we find that with a not too strict regulation for any VaR-constraint with an arbitrary risk level, there exists an ES-constraint leading to the same investment strategy, which shows on some level the ineffectiveness of the ES-based regulation. This differs from the conclusion drawn in Basak and Shapiro (2001) for the concave optimization problem, where VaR and ES lead to different solutions and ES provides a better loss protection.

This paper investigates the optimal information design for a system to minimize congestion cost in the presence of both autonomous vehicles (AVs) and human-driven vehicles (HVs). We incorporate asymmetric information between AVs and HVs in a routing game where there are two routes available and one of them has a state-dependent congestion cost. AVs are informed of the state and make choices as a fleet while HVs rely on information provided by the system and make self-interested choices. The system designs information in a Bayesian persuasion manner aiming to mitigate HVs' selfish routing such that traffic congestion cost is minimized. We show that the penetration of AVs can mitigate HVs' overcrowding problem and the first-best can be achieved when the fleet size of AVs reaches a high level. We find that it is optimal for the system to randomize in providing traffic information rather than to provide perfect information to HVs. When the information distortion is mild, HVs overcrowd the more desirable route as in the complete information benchmark. When the information distortion is strong enough, their behaviors are flipped and overcrowd the less desirable route. Interestingly, our research sheds light on the interaction of AV platooning and information provision. Finally, credibility constraint limits the social planner's persuasion power in navigating HVs away from overcrowding the more desirable route. When the fraction of AVs is high enough, the first-best can be achieved through the synergy of AV platooning and information provision.

In an election campaign, candidates must decide how to optimally allocate their efforts/resources optimally among the regions of a country. As a result, the outcome of the election will depend on the players' strategies and the voters' preferences. In this work, we present a zero-sum game where two candidates decide how to invest a fixed resource in a set of regions, while considering their sizes and biases. We explore the Majority System (MS) as well as the Electoral College (EC) voting systems. We prove equilibrium existence and uniqueness under MS in a deterministic model; in addition, their closed form expressions are provided when fixing the subset of regions and relaxing the non-negative investing constraint. For the stochastic case, we use Monte Carlo simulations to compute the players' payoffs, together with its gradient and hessian. For the EC, given the lack of Equilibrium in pure strategies, we propose an iterative algorithm to find Equilibrium in mixed strategies in a subset of the simplex lattice. We illustrate numerical instances under both election systems, and contrast players' equilibrium strategies. Finally, we show that polarization induces candidates to focus on larger regions with negative biases under MS, whereas candidates concentrate on swing states under EC.

This chapter studies the challenges a cryptocurrency faces to become a common means of exchange. In particular, the paper discusses the scalability constraint that limits the number of transactions a cryptocurrency may be able to verity per unit of time, the network effect in goods that function as money that increases the cost of new currencies to gain market share, and the implications of the fixed monetary rule present in most cryptocurrencies that departs from an elastic optimal monetary policy. Potential solutions for each case are also discussed.

A financial constraint that prevents access to external funds induces non-classical measurement error in average q as a proxy for unobservable marginal q. Unlike classical measurement error, this measurement error biases upward the coefficient on average q in a univariate regression of investment on average q. In a multiple regression of investment on average q and cash flow, the coefficient on cash flow is positive. The positive cash-flow coefficient indicates the presence of a financial constraint, but it does not indicate a shortage of liquidity to fund current investment. In addition, the coefficient on average q is biased downward. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Alzheimer ailment is forward moving mind issue dementia, neurodegenerative, irreversible infection. Dementia is fundamental driver of Alzheimer's sickness which impact the day by day life, for example, loss of psychological practical reasoning, and social exercises. Presently days, there is constraint to foresee which individual related with gentle subjective impedance changed over into Alzheimer's illness. Multidimensional surface investigation utilizing computerized reasoning, picture preparing and AI calculations [1,2] to distinguish the examples that can't catch at visual examination. In this paper, outline of Artificial Intelligence and its application over medicinal pictures, ongoing difficulties, more profound mechanical foundation in regards to advancement/development human services, genuine clinical application with parameters of conclusion, improve persistent well being and highlight based sickness ID. It gives procedures in the field of medicinal imaging by creating self-propelled and shrewd calculation that oversees and extricates significant data out of crude information in type of pictures. It helps in early analysis and treatment of infection. In this paper center around different angle and issues of developing job of restorative picture, basic specialized bit of leeway of AI over conventional examples for improvement of individual. For end, per user comprehends further innovative angles for keen calculations for highlight determination of early conclusion of Alzheimer's patient consideration.

Using optimising policy framework, we build and estimate a small-scale DSGE model of the US, and tell the story of monetary and fiscal policy interactions in 1955-2018. We find that fiscal policy is important to identify shifts in monetary policy preferences, and it is shaped by the political color. We use this model to analyse the episode of zero lower bound on interest rate in 2008-15. We find that the bound constrained monetary policy, explain some observed irregularities in macroeconomic data, and demonstrate that a change to price level targeting could have generated a powerful lift-off from the constraint.

The standard SEIR model based on a parameterization consistent with the international evidence cannot explain the very high COVID-19 related mortality in Lombardy. This paper proposes an extension of the standard SEIR model that is capable of solving the puzzle. The SEIR model features exogenous mortality: once Susceptible individuals become first Exposed, and then Infected, they succumb with a given probability. The extended model inlcudes an Hospitlization process and the possibility that Hospitalized patients, who need to resort to Intensive Care Unit, cannot find availability because the ICU is saturated. This Constraint creates an additional increase in mortality, which is endogenous to the diffusion of the disease. The SEIHCR (H stands for Hospitalization and C stands for Constraint) is capable of explaining the dynamics of COVID-19 related mortality in Lombardy with a paramerization consistent with the international evidence.

With increasing availability of consumer data and rapid advancement and applications of technologies, online retailers are gaining better knowledge of shopping behavior and preferences of their customers. Thus more and more retailers are providing customized product assortment to better match the needs of customers and generate more sales. In this paper, we study a two-stage revenue management model where a retailer decides non-customized price discounts at stage one due to fairness consideration and customized product assortment at stage two (upon the arrival of customers) under the multinomial logit choice model. We employ a robust approach for the joint discounts and customized assortment optimization problem to handle data uncertainty for estimating customer preferences and distribution of different customer segments. We analyze the structural properties of the problems and propose efficient computational methods to solve the problems with/without cardinality constraint on the assortment. In certain cases, our algorithm converges at a superlinear rate. When there is a cardinality constraint on the assortment, we find the retailer should offer deeper discounts as the constraint becomes more restrictive. We also provide some discussion on the value of our robust solution and the extension when the customer discount sensitivity function is also uncertain. Finally, our extensive numerical study shows that the solutions under the robust approach perform very well compared to the one assuming accurate information and has robustness when there is uncertainty.

We develop a novel large-scale Network Autoregressive model with balance Constraint (NAC) to predict hour-ahead gas flows in the gas transmission network, where the total in- and out-flows of the network are balanced over time. By integrating recent advances in optimization and statistical modeling, the NAC model can provide an accurate hour-ahead forecast of the gas flow at all of the distribution points in the network. By detecting the influential nodes of the dynamic network, taking into account that demand and supply have to be balanced, the forecast can be used to compute an optimized schedule and resource allocation. We demonstrate an application of our model in forecasting hour-ahead gas in- and out-flows at 128 nodes in the German high-pressure natural gas transmission network over a time frame of 22 months. It dramatically improves the out-of-sample forecast accuracy with the average root mean squared error reduced from 1.116 to 0.725 (35% change) and the mean squared forecast error reduced from 36.389 to 11.914 (67% change). The NAC model successfully also improves the demand and supply balance, with the average deviation dropping from 7.590 to 2.391. Moreover, we identify three permanently influential nodes in the gas transmission network, and we also capture the dynamic changes in the network with 42 influential nodes on average and 17 influential nodes in summer months.

Fractional Kelly portfolios are popular investment strategies in the market. In this paper, we improve the mean-variance efficiency of a fractional Kelly portfolio by minimizing the variance of the return of a portfolio subject to the constraint that the expected return rate of the portfolio is as high as that of the fractional Kelly portfolio. We consider so-called equilibrium portfolio strategies due to time inconsistency caused by the mean-variance criterion. We drive an equilibrium strategy in closed form and show that it reduces the variance of portfolio return compared to the fractional Kelly portfolio, although the reduction is quantitatively small.

We introduce and solve an optimal asset allocation problem under a weighted expected shortfall (WES) constraint, which contains the risk management problem under an expected shortfall constraint of Basak and Shapiro (2001) as a special case. Furthermore, we link our risk management problem under the WES constraint with an optimal asset allocation with a multiple-reference-based preference (MRBP) and find that the optimal wealth with MRBP owns the same form as the optimal solution under the WES constraint. For the degenerate case with a fixed reference level, we are able to determine the critical maximal allowed expected shortfall constraint as a function of the loss aversion parameters to achieve equivalence. It is interesting to observe that, while no equivalence can be in general obtained between the WES and the MRBP solution, the optimal terminal wealth of the WES can be made to coincide with the MRBP terminal wealth in the most favorable and in the worst market states. In addition, we carry out a general equilibrium analysis in the presence of a WES/MRBP risk manager.

Advertisement, as a major revenue source of e-commerce platforms, is an important online marketing tool for sellers thereof. In this paper, we explore the dynamic ad allocation with limited slots upon each customer arrival for e-commerce platforms when the advertisers specify, for each ad, the budget constraint, the time periods when the ad should be displayed, and the click-through (lower-limit) constraint for certain customer segments. The goal of the platform is to maximize its payoff over the entire horizon. We propose a two-stage stochastic program framework in which the platform first decides the click-through goals for each ad/customer-type pair, and then devises the ad allocation policy to satisfy these goals in the second stage. We show that the optimal click-through goals can be achieved efficiently by solving a convex program, which can further reduce to a scalable linear program if the customer click-through behavior follows the multinomial logit model. Moreover, we provide a family of debt-weighted algorithms to achieve the optimal click-through goals, and prove that they are asymptotically optimal when the problem size scales to infinity. Compared to choice-based linear programming and its variant, our approach has better scalability and can deplete the ad budgets more smoothly throughout the horizon, which is very much desirable for the online advertising business in practice.

We develop an algorithm that makes it possible to generate all correlation matrices satisfying a constraint on their average value. We extend the results to the case of multiple constraints. These results can be used to assess the extent to which methodologies driven by correlation matrices are robust to misspecification thereof.

This paper presents a proposal for improving corporate governance. It is designed to increase incentives for corporations and their leaders to adhere to the constraint that Milton Friedman laid out in his 1970 New York Times article, “The Social Responsibility of Business Is to Increase Its Profits.” The constraint, which I call the Friedman Constraint, is that in conducting its business the firm should conform to the “basic rules of the society, both those embodied in law and those embodied in ethical custom.” The increased incentives are established by creating an entity that has a board seat and is thus a corporate insider. The entity has significant financial incentives to minimize corporate fines for wrongdoings, but receives none of the potential benefits of any bad conduct.

We propose a class of dynamic models that capture subjective (and hence unobservable) constraints on the amount of information a decision maker can acquire, pay attention to, or absorb, via an Information Choice Process (icp). An icp specifies the information that can be acquired about the payoff-relevant state in the current period, and how this choice affects what can be learned in the future. In spite of their generality, wherein icps can accommodate any dependence of the information constraint on the history of information choices and state realizations, we show that the constraints imposed by them are identified up to a dynamic extension of Blackwell dominance. All the other parameters of the model are also uniquely identified. Behaviorally, the model is characterized by a novel recursive application of static properties.

Estimating the pose of objects with circle feature from images is a basic and important question in computer vision community. This paper is focused on the ambiguity problem in pose estimation of circle feature, and a new method is proposed based on the concentric circle constraint. The pose of a single circle feature, in general, can be determined from its projection in the image plane with a pre-calibrated camera. However, there are generally two possible sets of pose parameters. By introducing the concentric circle constraint, interference from the false solution can be excluded. On the basis of element at infinity in projective geometry and the Euclidean distance invariant, cases that concentric circles are coplanar and non-coplanar are discussed respectively. Experiments on these two cases are performed to validate the proposed method.

This paper studies the dynamic mean-risk portfolio optimization problem with variance and Value-at-Risk(VaR) as the risk measures in recognizing the importance of incorporating different risk measures in the portfolio management model. Using the martingale approach and combining it with the quantile optimization technique, we provide the solution framework for this problem and show that the optimal terminal wealth may have different patterns under a general market setting. When the market parameters are deterministic, we develop the closed-form solution for this problem. Examples are provided to illustrate the solution procedure of our method and demonstrate the beneft of our dynamic portfolio model comparing with its static counterpart.

TThis paper describes a study of the two-sided matching problem with type-specific maximal and minimal quotas with the deferred acceptance mechanism in a student– supervisor assignment. In this problem, both students and supervisors were classified by type according to their affiliations, and the supervisors set type-specific maximal and minimal quotas. In the proposed mechanism, the maximal quotas were dynamically adjusted to fulfill the minimal quotas. We found that while the mechanism may not be strategy-proof, it eliminates justified envy among students of the same “type,” and it achieved feasibility with a certain distributional constraint. Moreover, if the sum of the ranks of students and supervisors in the final assignment is viewed as a measure of welfare, there is no domination relationship between our mechanism and the deferred acceptance mechanism. As evidence, the paper presents a case study of the proposed mechanism in use at a Japanese university.

User satisfaction is a critical determinant of information systems success. Prior research has long conceptualized it as a bipolar continuum with a “neutral” midpoint and “extremely dissatisfied” and “extremely satisfied” as endpoints. Therefore, the bipolar view imposes the constraint that an increase in satisfaction implies an equal and opposite decrease in dissatisfaction. Consequently, a key limitation of the bipolar conceptualization is its inability to account for simultaneous processing of positivity and negativity, i.e., ambivalence. A bivariate perspective relaxes this constraint, permitting the representation of mixed feelings, which allows potential additional insights. It represents satisfaction and dissatisfaction as distinct unipolar constructs each ranging from “neutral” to “extremely satisfied” or “extremely dissatisfied”, respectively. Users commonly experience mixed feelings, and the bipolar approach discards potentially useful information about the true affective state and distinct antecedents and consequences of satisfaction and dissatisfaction. Results of our survey (n=214) support the unipolar conceptualization. Unipolar satisfaction and dissatisfaction are moderately correlated -0.48, well below the -1.00 implied by the bipolar view. Unipolar satisfaction and dissatisfaction have unique and distinct effects on behavioral intention. Bipolar satisfaction explains no additional variance beyond the unipolar constructs. Together, these findings suggest that the unipolar conceptualizations can better represent user satisfaction and dissatisfaction. This study highlights the significance of ambivalence, advances the understanding of user satisfaction and dissatisfaction as distinct unipolar constructs, and provides a basis for new research into the potentially differential relationships to key antecedents and consequences in nomological networks relating actionable design and implementation actions to user behavioral outcomes.

We study the issue of decentralization from the implementation perspective. In most cases of institution design, a social planner is forced to operate in a decentralized manner, by designing distinct institutions that deal with different issues or sectors, over which agents may have complementarities in their preferences. By utilizing the notion of a rights structure, we consider a two-sector environment and examine the possibilities that arise in implementation when the social planner can condition the rights structure of one sector to the one of the other. We distinguish two cases, one when a sector constitutes an institutional constraint (constrained conditional implementation), and one where both sectors can be objects of design (conditional implementation). We characterize the social choice rules that are implementable in the first case, while in the second case we provide sufficient conditions for implementation. Our results imply that it is in general more difficult to implement a rule in a decentralized environment. As applications of our characterization theorems, we include some possibility results. First we prove the implementability of a weaker version of the stable rule in a constrained matching environment with partners and projects and second, we prove the implementability of the weak Pareto rule in a multi-issue environment with lexicographic preferences.

We develop a dynamic version of the SSVI parameterisation for the total implied variance, ensuring that European vanilla option prices are martingales, hence preventing the occurrence of arbitrage, both static and dynamic. Insisting on the constraint that the total implied variance needs to be null at the maturity of the option, we show that no model--in our setting--allows for such behaviour. This naturally gives rise to the concept of implied volatility bubbles, whereby trading in an arbitrage-free way is only possible during part of the life of the contract, but not all the way until expiry.

Security insurance against mining calculations, an advancing exploration territory, finds the pessimistic symptoms of information mining techniques that start from the protection dissemination of people and associations. Late research in security insurance information mining has focused on finding a legitimate soundness between data protection and learning revelation. In this paper, we present a calculation that creates a disinfected database which keeps up the best possible harmony between data security and information revelation. We initially create the significant sicknesses from the first database, at that point convert the first database into a cleaned (sanitized ) database through our proposed arbitrary cleaning(sanitization) process. Next, we assess the produced cleaned database based on Knowledge Discovery (KD) and Privacy Factor (PF). This assessment procedure computes the KD and PF by looking at the noteworthy illnesses created from the first database versus the sterilized database. On the off chance that the estimations of KD and PF are fulfilled by the client determined limit esteem, the sterilized database is prepared for distributing, else our proposed calculation rehashes the sterilization procedure until the KD and PF qualities are fulfilled. At long last, our proposed calculation is assessed by contrasting it and comparative well known calculations on the criteria of KD and PF. The trial results demonstrate that our proposed calculation performed well at 77% running time contrasted with comparative prevalent calculations. The memory prerequisite for our proposed calculation is 25%, while past calculations need 31% memory. Our proposed calculation additionally performed 53% superior to past calculations regarding number of critical sicknesses influenced by the proposed cleansing procedure.

Field experiments conducted with the village, city, state, region, or even country as the unit of randomization are becoming commonplace in the social sciences. While convenient, subsequent data analysis may be complicated by the constraint on the number of clusters in treatment and control. Through a battery of Monte Carlo simulations, we examine best practices for estimating unit-level treatment effects in cluster-randomized ˝field experiments, particularly in settings that generate short panel data. In most settings we consider, unit-level estimation with unit fixed effects and cluster-level estimation weighted by the number of units per cluster tend to be robust to potentially problematic features in the data while giving greater statistical power. Using insights from our analysis, we evaluate the effect of a unique ˝field experiment: a nationwide tipping field experiment across markets on the Uber app. Beyond the import of showing how tipping affects aggregate market outcomes, we provide several insights on aspects of generating and analyzing cluster-randomized experimental data when there are constraints on the number of experimental units in treatment and control.

In this paper, we introduce a new class of risk measures and the corresponding risk minimizing portfolio optimization problem. Instead of measuring the expected deviation of a daily return from a single target value, we propose to measure its deviation from a range of values centered on the single target value. By relaxing the definition of deviation, the proposed risk measure is robust to the variation of data input and thus the resulting risk-minimizing portfolio has a lower turnover rate and is resilient to outliers. To construct a practical portfolio, we propose to impose an l2-norm constraint on the portfolio weights to stabilize the portfolio's out-of-sample performance. We show that for some cases of our proposed range-based risk measures, the corresponding portfolio optimization can be recast as a support vector regression problem. This allows us to tap into the machine learning literature on support vector regression and effectively solve the portfolio optimization problem even in high dimensions. Moreover, we present theoretical results on the robustness of our range-based risk minimizing portfolios. Simulation and empirical studies are conducted to examine the out-of-sample performance of the proposed portfolios.

This paper examines the idea of freedom as it is discussed in the work of J. S. Mill and Hannah Arendt. Key concepts related to the idea of freedom are also addressed: the distinction between negative and positive liberty, constraint and the role of law (especially in the work of H. L. Hart). Originally delivered as a lecture, the paper provides an introduction to the political idea of freedom.

We analyze the effect of ambiguous loss probabilities on competitive insurance markets with asymmetric information. We characterize equilibria under actuarially fair pricing with preferences that are second-order ambiguity averse (have smooth indifference curves). We also show existence of uniqueness of the second-best contracts and provide a characterization. Non-increasing absolute ambiguity aversion is sufficient for adverse selection in the smooth model. We then determine the effect of ambiguity on equilibrium under ambiguity aversion. There is a coverage effect because ambiguity relaxes the self-selection constraint and raises the available coverage for the low risks, and an ambiguity effect because ambiguity makes ambiguity averse agents worse off. Both effects are conflicting when it comes to their impact on the critical pro-portion of high risks required for a Rothschild-Stiglitz equilibrium to exist and social welfare. We derive conditions that allow to resolve the indeterminate social welfare effect.

Operations researchers have long drawn insight from the structure of constraint coefficient matrices (CCMs) for mixed integer programs (MIPs). We propose a new question: Can pictorial representations of CCM structure be used to identify similar MIP models and instances? In this paper, CCM structure is visualized using digital images, and computer vision techniques are used to detect latent structural features therein. The resulting feature vectors are used to measure similarity between images and, consequently, MIPs. An introductory analysis examines a subset of the instances from strIPlib and MIPLIB 2017, two online repositories for MIP instances. Results indicate that structure-based comparisons may allow for relationships to be identified between MIPs from disparate application areas. Additionally, image-based comparisons reveal that ostensibly similar variations of an MIP model may yield instances with markedly different mathematical structures. Summary of Contribution: This paper presents a methodology for comparing mixed integer programs (MIPs) from any research domain based on the structure of the constraint coefficient matrices for one or more instances of a model. Specifically, computer vision and deep learning techniques are used to extract structural features and measure the similarity between these images. This process is agnostic to application area and instead focuses solely on mathematical structure. As a result, this methodology offers a fundamentally new way for operations researchers to view MIP similarity and highlights similarities between research problems that may have previously been viewed as unrelated.

We present a general framework for the design/optimization of materials that is capable of accounting for multiple information sources available to the materials designer. We demonstrate the framework through the microstructure-based design of multi-phase microstructures. Specifically, we seek to maximize the strength normalized strain-hardening rate of a dual-phase ferritic/martensitic steel through a multi-information source Bayesian optimal design strategy. We assume that we have multiple sources of information with varying degrees of fidelity as well as cost. The available information from all sources is fused through a reification approach and then a sequential experimental design is carried out. The experimental design seeks not only to identify the most promising region in the materials design space relative to the objective at hand, but also to identify the source of information that should be used to query this point in the decision space. The selection criterion for the source used accounts for the discrepancy between the source and the 'ground truth' as well as its cost. It is shown that when there is a hard constraint on the budget available to carry out the optimization, accounting for the cost of querying individual sources is essential.