The Stackelberg Games of Water Extraction with Myopic Agents

We consider a discrete-time, infinite-horizon dynamic game of groundwater extraction. A Water Agency charges an extraction cost to water users and controls the marginal extraction cost so that it depends not only on the level of groundwater but also on total water extraction (through a parameter [Formula: see text] that represents the degree of strategic interactions between water users) and on rainfall (through parameter [Formula: see text]). The water users are selfish and myopic, and the goal of the agency is to give them incentives so as to improve their total discounted welfare. We look at this problem in several situations. In the first situation, the parameters [Formula: see text] and [Formula: see text] are considered to be fixed over time. The first result shows that when the Water Agency is patient (the discount factor tends to 1), the optimal marginal extraction cost asks for strategic interactions between agents. The contrary holds for a discount factor near 0. In a second situation, we look at the dynamic Stackelberg game where the Agency decides at each time what cost parameter they must announce. We study theoretically and numerically the solution to this problem. Simulations illustrate the possibility that threshold policies are good candidates for optimal policies.

Effective Online Order Acceptance Policies for Omnichannel Fulfillment

Problem definition: Omnichannel retailing has led to the use of traditional stores as fulfillment centers for online orders. Omnichannel fulfillment problems have two components: (1) accepting a certain number of online orders prior to seeing store demands and (2) satisfying (or filling) some of these accepted online demands as efficiently as possible with any leftover inventory after store demands have been met. Hence, there is a fundamental trade-off between store cancellations of accepted online orders and potentially increased profits because of more acceptances of online orders. We study this joint problem of online order acceptance and fulfillment (including cancellations) to minimize total costs, including shipping charges and cancellation penalties in single-period and limited multiperiod settings. Academic/practical relevance: Despite the growing importance of omnichannel fulfillment via online orders, our work provides the first study incorporating cancellation penalties along with fulfillment costs. Methodology: We build a two-stage stochastic model. In the first stage, the retailer sets a policy specifying which online orders it will accept. The second stage represents the process of fulfilling online orders after the uncertain quantities of in-store purchases are revealed. We analyze threshold policies that accept online orders as long as the inventories are above a global threshold, a local threshold per region, or a hybrid. Results: For a single period, total costs are unimodal as a function of the global threshold and unimodal as a function of a single local threshold holding all other local thresholds at constant values, motivating a gradient search algorithm. Reformulating as an appropriate linear program with network flow structure, we estimate the derivative (using infinitesimal perturbation analysis) of the total cost as a function of the thresholds. We validate the performance of the threshold policies empirically using data from a high-end North American retailer. Our two-location experiments demonstrate that local thresholds perform better than global thresholds in a wide variety of settings. Conversely, in a narrow region with negatively correlated online demand between locations and very low shipping costs, global threshold outperforms local thresholds. A hybrid policy only marginally improves on the better of the two. In multiple periods, we study one- and two-location models and provide insights into effective solution methods for the general case. Managerial implications: Our methods provide effective algorithms to manage fulfillment costs for online orders, demonstrating a significant reduction over policies that treat each location separately and reflecting the significant advantage of incorporating shipping in computing thresholds. Numerical studies provide insights as to why local thresholds perform well in a wide variety of situations.

On the Cluster Admission Problem for Cloud Computing

Cloud computing providers face the problem of matching heterogeneous customer workloads to resources that will serve them. This is particularly challenging if customers, who are already running a job on a cluster, scale their resource usage up and down over time. The provider therefore has to continuously decide whether she can add additional workloads to a given cluster or if doing so would impact existing workloads’ ability to scale. Currently, this is often done using simple threshold policies to reserve large parts of each cluster, which leads to low efficiency (i.e., low average utilization of the cluster). We propose more sophisticated policies for controlling admission to a cluster and demonstrate that they significantly increase cluster utilization. We first introduce the cluster admission problem and formalize it as a constrained Partially Observable Markov Decision Process (POMDP). As it is infeasible to solve the POMDP optimally, we then systematically design admission policies that estimate moments of each workload’s distribution of future resource usage. Via extensive simulations grounded in a trace from Microsoft Azure, we show that our admission policies lead to a substantial improvement over the simple threshold policy. We then show that substantial further gains are possible if high-quality information is available about arriving workloads. Based on this, we propose an information elicitation approach to incentivize users to provide this information and simulate its effects.

Evaluating Criteria-Based Thresholds in Cataract Commissioning and Their Contribution to Variable Access to Surgery – an Observational Study

ObjectivesPrevious work has suggested wide variation in policies for cataract surgery across different Commissioning Groups, but did not evaluate the potential impact of that variation on access.This study characterises the variation in rates of cataract surgery across England, reviews threshold policies against NICE guidance, and explores whether stringency of policy has a significant effect on access, to determine whether threshold policies are contributing to unequal access to surgery. It examines the effect of social deprivation and the impact of prior approval processes, where these are in place.MethodsInformation on number of surgeries undertaken and threshold policy were provided from 127 Clinical Commissioning Groups (“CCGs”) through Freedom of Information request. The results were grouped by threshold stringency and analysed on an age group-corrected basis. ANOVA testing was performed to assess effect of policy stringency on regional rates of cataract surgery.ResultsIn the population over 60 years old, rates of cataract surgery vary across CCGs, from 1,980 to 6,427 per 100,000 population with a standard deviation (784.76) of 22% of the mean value, 3,598.There is variability in threshold policies for cataract surgery between CCGs: 33 had no policy, 45 utilised NICE-compliant policies, accessible on the basis of Quality of Life (“QoL”) impact, and 39 required that Visual Acuity (“VA”) threshold be exceeded, against current NICE guidance. Increasing restrictiveness of policy is associated with decreasing rates of cataract surgery (p<0.01) and accounts for 18% of the total variation seen. Variation in deprivation across CCGs contributes to 11% of the total deviation (p<0.01).There is little evidential basis to many policies, with 40% of policies not citing any supporting evidence. Prior approval processes represent 7.3% of total cataract activity but are not significantly associated with a reduced rate of cataract surgery (p=0.56).ConclusionOver two-thirds of CCGs continue to use threshold-based policies for access to cataract surgery, with increasing stringency of policy associated with decreasing cataract activity. A third of CCGs control access solely on the basis of visual acuity requirements, despite NICE guidance to the contrary. There is a need for consistency in policy across CCGs, and introduction of validated quality of life impact assessment tool to reduce variability of access.

When strong mitigation against a pandemic backfires

I introduce social feasibility constraints in a SIR epidemiological model: at any point in time, the ability of a social planner to impose mitigation measures is limited, but it is increasing in the proportion of infected individuals. When considering threshold policies with constant levels of mitigation for a time period, the overall fatality rate in the population is non-monotonic in the levels of mitigation: higher levels of mitigation can increase the overall fatality rate. Intuitively, strong mitigation at a point in time can undermine the social feasibility of future mitigation.

Optimal Financing and Disclosure

I study an entrepreneur who finances a project with uncertain cash flows and who jointly chooses the disclosure and financing policies. In the Bayesian persuasion framework, I show that it is optimal to truthfully reveal whether the project’s cash flows are above a threshold. This class of threshold policies is optimal for any prior belief, monotone security, and increasing utility function of the entrepreneur. I characterize how the disclosure threshold depends on the underlying security, the prior, and the cost of investment. The financing choice of the entrepreneur is determined by a new trade-off between the likelihood of persuading investors and relinquishing cash flow rights. Absent further frictions, the optimal security is indeterminate. If there is adverse selection after the entrepreneur has disclosed information, the unique outcome is a pooling equilibrium in which the entrepreneur pledges the entire cash flow. This paper was accepted by Tomasz Piskorski, finance.

ASPEK HUKUM TERHADAP SELEKSI PENERIMAAN CPNS TAHUN 2018

Civil servant candidates (CPNS) recruitment is an important series of activities in order to have a Civil Servant with integrity, professionalism, neutral, free from political intervention, free from corrupt, collusion and nepotism (KKN) practices, and is able to provide public services to the community as well as to be a role model to promote national unity based on Pancasila and the 1945 Constitution of the Republic of Indonesia. Therefore, in the selection process the organizers need to carefully prepare everything to minimize the possible legal polemic. Issue related to threshold policies that suddenly change was fully discussed in the last civil servant enrollment test (CPNS) 2018. That change cannot be separated from the discussion of legal aspects when there are some CPNS participants who feel disadvantaged. Basically policy is made to be in line laws and regulations, thus Ministry for the Empowerment of The State Apparaturs Regulation (Permenpan) 37/2018 and Permenpan 61/2018, policies should provide a sense of justice, legal certainty and usefulness.