Single Decision Maker Research Articles

On the Properties of Yield Distributions in Random Yield Problems: Conditions, Class of Distributions and Relevant Applications

In this study, we propose two technical assumptions to ensure the unimodality of the objective functions in two classes of price and quantity decision problems with one procurement opportunity under supply random yield and deterministic demand in a price‐setting environment. The first class of problems involves a decentralized supply chain/assembly system under different configurations, and the second class focuses on a single firm's price and quantity decisions under different contracts, payment schemes and supplier portfolios. We provide appealing economic interpretations and easy‐to‐verify sufficient conditions for our proposed technical assumptions. We show that both assumptions are preserved under truncation and positive scale, and satisfied by most commonly used continuous yield distributions. Moreover, similar to the role that the increasing generalized failure rate (IGFR) property plays in analyzing operations problems with demand uncertainty, our Assumption 1 plays a fundamental role in regulating the behaviors of the objective functions for both classes of random yield problems. Assumption 2 is more general than both Assumption 1 and the IGFR property and is used to analyze the second class of problems. Finally, we discuss the difference between random yield and random demand problems, and explain the rationale for the need of different technical assumptions.

ALGORITMA FUZZY GOAL PROGRAMMING UNTUK MASALAH PEMROGRAMAN BILEVEL MULTIOBJEKTIF

Bilevel multiobjective programming problems are mathematical programming that solves the problem of planning with two decision makers (DM) in two level or hierarchical organization with the objective function of each organization can be more than one. In this paper, we discussed the special case of this problem with single decision maker at the upper level and multiple decision makers at the lower level. This problem can be solved using fuzzy goal programming (FGP) approach. In this approach, the membership function for the defined fuzzy goals of all objective functions of DMs at the two levels was developed first in the model formulation of the problem. Thus the membership function for vector of fuzzy goals of the decision variables controlled by the leader. Then FGP approach requires the leader to set goals for each objective that he/she wishes to attain. A preferred solution is then defined for minimizes the deviations from the set of goals. Numerical example is provided to illustrate the approach.

Linking forest policy issues and decision support tools in Europe

While very many decision-support (DS) tools (i.e. models and decision support systems (DSS)) have been developed to address forest management problems in Europe, the use of such tools in supporting forest policy processes remains limited. Additionally, while there has been very limited sharing of these tools between European countries, there may be an untapped potential for both users and developers in this area.This paper focuses on improving understanding and capacities in the use of forest DS tools for decision making by identifying major forest policy areas, tools available to support them, compatibility of existing tools with the requirements of forest policy areas, potential areas where tools may be shared between countries and factors limiting the use of DS tools in forest policy.Data collection was based on expert interviews. The questionnaire, which comprised a combination of open- and close-ended questions, was forwarded to experts via email. Expert interviews were completed via Skype with the input of one policy specialist and one modeller/decision support specialist from each country.This study categorised key forest policy areas and the DS tools available to support them. Almost one third of these forest policy areas were not addressed by any DS tool. The analysis also revealed that DS tools are mainly developed to assist scientists and policy decision makers to address smaller spatial scales, that they are more orientated to single decision makers with a predominant focus on market wood products. In addition, through an attribute-matching exercise, the DS tools that could potentially be used in other countries to support similar forest policy areas were also identified.Interviews highlighted some of the reasons why DS tools are seldom used in policy making processes; these include a lack of trust in the actual use of the tools as well as a perception of inadequacy for the specifics of real policy process. This research provides a detailed overview of existing DS tools and the forest policy areas that they address. It further provides information on how to address or reduce the gap between DS tools functionalities and requirements from policy makers.

Intertemporal Decision-Making and the Nash Bargaining Solution

Most applications of real options analysis assume a single decision-maker uses flexibility to maximize a firm's market value. This paper presents an alternative approach suitable for firms with two utility-maximizing decision-makers who have joint responsibility for setting firm policies. In this approach the Nash bargaining solution is used to determine the outcome at each date in a way that ensures policies are dynamically consistent. It is illustrated using resource-extraction and liquidity-management problems.

Maximum income resulting from energy arbitrage by battery systems subject to cycle aging and price uncertainty from a dynamic programming perspective

This paper describes an approach to compute the maximum value of energy storage systems (ESS) in grid applications under uncertain energy prices. The value obtained is based on an optimal operation (consisting of charge/discharge sequences) of the ESS. In other words, it is the maximum value that may be obtained when the ESS charge/discharge sequence is adapted to the expected operational conditions.To obtain that optimal value, this paper describes a dynamic program approach, with the particularity that the switching decisions are optimized considering an uncertain price evolution and a dynamic calculation of the aging cost. A practical implementation of this approach is proposed, in which the problem is conveniently sliced into matrices corresponding to single decisions. It is shown that such an arrangement, combined with shift and re-indexing operators, provides a fast solution to the optimization problem consisting of a huge number of decision evaluations. The algorithm is then applied to a number of European electricity markets, with a particular focus on arbitrage. The particularities of the algorithm solutions are analyzed, and it is shown that not considering the imperfect foresight and the aging impacts leads to considerable errors in valuing an ESS.

A strategic model of job arrivals to a single machine with earliness and tardiness penalties

ABSTRACTWe consider a game of decentralized timing of jobs to a single server (machine) with a penalty for deviation from a due date, and no delay costs. The jobs’ sizes are homogeneous and deterministic. Each job belongs to a single decision maker, a customer, who aims to arrive at a time that minimizes his(her) deviation penalty. If multiple customers arrive at the same time, then their order of service is determined by a uniform random draw. We show that if the cost function has a weighted absolute deviation form, then any Nash equilibrium is pure and symmetric, that is, all customers arrive together. Furthermore, we show that there exist multiple, in fact a continuum, of equilibrium arrival times, and provide necessary and sufficient conditions for the socially optimal arrival time to be an equilibrium. The base model is solved explicitly, but the prevalence of a pure symmetric equilibrium is shown to be robust to several relaxations of the assumptions: restricted server availability, inclusion of small waiting costs, stochastic job sizes, randomly sized population, heterogeneous due dates, and nonlinear deviation penalties.

Endogeneity in Strategy-Performance Analysis: An Application to Initial Human Capital Strategy and New Venture Performance

Managers engage in a variety of strategies, not randomly, but having in mind their performance implications. Therefore, strategic choices are endogenous in performance equations. Despite increasing efforts by various scholars in solving endogeneity bias, prior attempts have almost exclusively focused on single, one-sided, and discrete (binary) organizational decisions. Yet, in reality, managers often face multiple, simultaneous, and interdependent decisions, possibly including a continuous choice set. These choices may further entail a two-sided process between managers and others, such as employees, strategic partners, customers, or investors, whose choices and preferences also affect the final decision. We discuss how endogeneity can plague the measurement of the performance effects of these two-sided strategic decisions—which are more complex, but more realistic, than prior representations of organizational decision making. We provide an empirical demonstration of possible methods to deal with three different sources of bias, by analyzing the performance effects of two human capital choices made by founders at startup: the size and average quality of the initial workforce.

Inference of Preference Heterogeneity from Choice Data

Suppose that an analyst observes inconsistent choices from either a single decision-maker, or a population of agents. Can the analyst determine whether this inconsistency arises from choice error (imperfect maximization of a single preference) or from preference heterogeneity (deliberate maximization of multiple preferences)? I model choice data as generated from imperfect maximization of a small number of preferences. The main results show that (a) simultaneously minimizing the number of inferred preferences and the number of unexplained observations can exactly recover the number of underlying preferences with high probability; (b) simultaneously minimizing the richness of the set of preferences and the number of unexplained observations can exactly recover the choice implications of the decision maker's underlying preferences with high probability.

A fuzzy MCDM-based approach for renewable energy technologies prioritisation

The main objective of this study is to build a decision model adapted to prioritise renewable energy technologies by applying fuzzy multi-criteria decision-making (MCDM) methods. A comprehensive set of criteria is presented based on sustainability due to its importance in the long-term, particularly in the field of energy which is a key element of sustainable development. They are classified into five categories such as technical, economic, environmental, social, and political criteria. This model is then operationalised in the scope of solar energy technologies in Iran. This paper examines and compares the results of fuzzy TOPSIS and fuzzy PROMETHEE based on a single decision maker. The results show that rather than the two technologies, others in both methods have moderate differences in ranking. However, technological decisions are preferred to be made based on FTOPSIS results because it is considered to be more realistic.

A fuzzy MCDM-based approach for renewable energy technologies prioritisation

The main objective of this study is to build a decision model adapted to prioritise renewable energy technologies by applying fuzzy multi-criteria decision-making (MCDM) methods. A comprehensive set of criteria is presented based on sustainability due to its importance in the long-term, particularly in the field of energy which is a key element of sustainable development. They are classified into five categories such as technical, economic, environmental, social, and political criteria. This model is then operationalised in the scope of solar energy technologies in Iran. This paper examines and compares the results of fuzzy TOPSIS and fuzzy PROMETHEE based on a single decision maker. The results show that rather than the two technologies, others in both methods have moderate differences in ranking. However, technological decisions are preferred to be made based on FTOPSIS results because it is considered to be more realistic.

Interactive Fuzzy Decision Making Algorithm for Two Level Linear Fractional Programming Problems

This paper offers an interactive fuzzy decision-making algorithm for solving two-level linear fractional programming (TLLFP) problem which contains a single decision maker at the upper level and multiple decision makers at the lower level. In the presented interactive mechanism, the fuzzy goals and associated weight of the objective at all levels are first determined and the satisfactory solution is attained by renewing the satisfactory degrees of decision makers including the overall satisfactory balance among all levels. Moreover, the value of distance function is used in order to verify the satisfaction grades. Finally, a numerical example is given to illustrate the performance of the presented algorithm.

Information processing by networks of quantum decision makers

We suggest a model of a multi-agent society of decision makers taking decisions being based on two criteria, one is the utility of the prospects and the other is the attractiveness of the considered prospects. The model is the generalization of quantum decision theory, developed earlier for single decision makers realizing one-step decisions, in two principal aspects. First, several decision makers are considered simultaneously, who interact with each other through information exchange. Second, a multistep procedure is treated, when the agents exchange information many times. Several decision makers exchanging information and forming their judgment, using quantum rules, form a kind of a quantum information network, where collective decisions develop in time as a result of information exchange. In addition to characterizing collective decisions that arise in human societies, such networks can describe dynamical processes occurring in artificial quantum intelligence composed of several parts or in a cluster of quantum computers. The practical usage of the theory is illustrated on the dynamic disjunction effect for which three quantitative predictions are made: (i) the probabilistic behavior of decision makers at the initial stage of the process is described; (ii) the decrease of the difference between the initial prospect probabilities and the related utility factors is proved; (iii) the existence of a common consensus after multiple exchange of information is predicted. The predicted numerical values are in very good agreement with empirical data.

Teams in a public goods experiment with punishment

Challenges addressed in global politics, such as climate change, maritime piracy and fighting highly contagious diseases, concern global public goods. The related policy decisions are primarily made by teams. In contrast, economic models of global public goods typically assume a single rational decision maker. We use a laboratory experiment to compare team decisions to the decisions of individuals in a finitely repeated public goods game with and without a costly punishment option. Teams of three participants coordinate on decisions either by majority or unanimity rule. We find that, in the absence of a punishment option, teams deciding by the unanimity rule contribute slightly more than individuals. With the punishment option, unanimity teams choose significantly lower levels of punishment and exhibit anti-social punishment less frequently than individuals. A possible explanation is the elimination of extreme preferences for punishment through the coordination process in teams.

Work is Important. Commentary on Finn etal. (2017) Decisions to Attend and Drink at Party Events: The Effects of Incentives and Disincentives and Lifetime Alcohol and Antisocial Problems.

In their recent study Finn et al. explore the effect of contextual factors and antisocial problems in alcohol use decision making(Finn et al., 2017). Both their findings as well as the methodology they use are inspiring. First, the findings point to the importance of including alcohol-related context in task design when studying decision making processes in alcohol use disorders. This could potentially result in more accurate assessments of real life decision making, i.e. tapping into a more ecologically valid decision-making process than when using a single monetary risk or decision-making task. Second, their findings highlight the importance of assessing personality traits when evaluating an individual's decision patterns towards alcohol use. Finally, the findings suggest that a low antisocial problems level and having social responsibilities such as work, might be factors enabling the feasibility of alcohol reduction as a treatment goal. This article is protected by copyright. All rights reserved.

Pareto Optimal Decision Making in a Distributed Opportunistic Sensing Problem.

We extend prior results on a single decision maker opportunistic sensing problem to a distributed, multidecision maker setting. The original formulation of the problem considers how to opportunistically use "in-flight" sensors to maximize target coverage. In that paper, the authors show that this problem is NP-hard with a strong polynomial heuristic for a single decision maker. This paper extends this by considering a distributed decision making scenario in which multiple independent parties attempt to simultaneously engage in opportunistic sensor assignment while managing interassignment conflict. Specifically, we develop an algorithm that: 1) produces a Pareto optimal opportunistic sensor allocation; 2) requires fewer bits of communicated information than a completely centralized deconfliction approach; and 3) runs in distributed polynomial time once the individual decision makers identify their preferred (optimal) sensor allocations. We validate these claims using appropriate simulations.

Integrated natural resource management as pathway to poverty reduction: Innovating practices, institutions and policies

Poverty has many faces and poverty reduction many pathways in different contexts. Lack of food and income interact with lack of access to water, energy, protection from floods, voice, rights and recognition. Among the pathways by which agricultural research can increase rural prosperity, integrated natural resource management deals with a complex nexus of issues, with tradeoffs among issues that are in various stages of denial, recognition, analysis, innovation, scenario synthesis and creation of platforms for (policy) change. Rather than on a portfolio of externally developed ‘solutions’ ready for adoption and use, the concept of sustainable development may primarily hinge on the strengths and weaknesses of local communities to observe, analyse, innovate, connect, organize collective action and become part of wider coalitions. ‘Boundary work’ supporting such efforts can help resolve issues in a polycentric governance context, especially where incomplete understanding and knowledge prevent potential win-win alternatives to current lose-lose conflicts to emerge. Integrated research-development approaches deal with context (‘theory of place’) and options (‘theory of change’) in multiple ways that vary from selecting sites for studying pre-defined issues to starting from whatever issue deserves prominence in a given location of interest. A knowledge-to-action linkage typology recognizes three situations of increasing complexity. In Type I more knowledge can directly lead to action by a single decision maker; in Type II more knowledge can inform tradeoff decisions, while in Type III negotiation support of multiple knowledge+multiple decision maker settings deals with a higher level of complexity. Current impact quantification can deal with the first, is challenged in the second and inadequate in the third case, dealing with complex social-ecological systems. Impact-oriented funding may focus on Type I and miss the opportunities for the larger ultimate impact of Type II and III involvements.

Dynamic choices are most accurate in small groups

According to the classic results of Galton and Condorcet, as well as in modern decision-making models, accuracy in groups increases with group size. However, these studies do not consider the naturally occurring situation in which individuals dynamically re-evaluate their decision with a possible change of opinion. The dynamics of re-evaluation in groups are very different to individual re-evaluation because individuals influence the group and the group influences the individual. We find that individual accuracy in a group is higher when individuals re-evaluate because all members have more access to social information, while in single decisions, those deciding first have less. This improvement is smaller in large groups as in this case errors can cascade across the members of the group before re-evaluation can correct them. The net result is a maximal accuracy at a small group size. We also analyzed the case in which individuals are influenced only by a small number of the other individuals. In this case, cascading errors affect the interacting subgroups but are very unlikely to reach the whole group. This results in a local optimum at a small group size but also an optimum at a very large size. We thus suggest that re-evaluation dynamics can make small and very large groups optimal. Also, features that may be seen as limitations, like an influence from only a small number of individuals, may turn to be beneficial when considering local animal interactions, here filtering out cascading of errors in the group when reconsideration dynamics takes place.

MiR-221-regulated KIT level by wild type or leukemia mutant RUNX1: a determinant of single myeloblast fate decisions that - collectively - drives or hinders granulopoiesis.

RUNX1, a master transcription factor of hematopoiesis, was shown to orchestrate both cell proliferation and differentiation during granulopoiesis by regulating microRNAs (miRs). In this study, taking advantage of the miR-ON reporter system, we monitored first, how the granulocyte colony stimulation factor (GCSF) temporally modulates the concomitant level variation of miR-221 and one of its prototypic targets, the stem cell factor receptor KIT, in single 32DmiR-ON-221 myeloblasts expressing wild type RUNX1. Second, with the same reporter system we assessed how these temporal dynamics are affected by the t(8;21)(q22;q22) acute myelogenous leukemia mutant RUNX1-MTG8 (RM8) in single 32D-RM8miR-ON-221 myeloblasts. Depending on either wild type, or mutant, RUNX1 transcriptional regulation, the cell-context specific miR-221-regulated KIT level translates into differential single cell fate decisions. Collectively, single cell fate choices translate into either initial expansion of undifferentiated myeloblasts followed by terminal granulocyte differentiation, as it happens in normal granulopoiesis, or aggressive growth of undifferentiated myeloblasts, as it happens in RUNX1-MTG8-positive acute myelogenous leukemia. Increasing knowledge of biological changes, due to altered miRNA dynamics, is expected to have relevant translational implications for leukemia detection and treatment.

Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-by-cell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyte–erythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships.

2191

OBJECTIVES/SPECIFIC AIMS: (1) To propose an iterative decision-making model of care planning for CSHCN. (2) To identify targets warranting measurement in future studies of SDM in care planning for CSHCN. METHODS/STUDY POPULATION: Conceptual model developed by a multidisciplinary team iteratively considering the complex relationships among diverse factors affecting care planning for CSHCN, informed by clinical and implementation science experience and a scoping literature review of medical and cognitive sciences literature addressing interpersonal decision-making, communication, negotiation, and trust among children, their parents, and their clinicians. RESULTS/ANTICIPATED RESULTS: Decision-making interventions in pediatrics tend to focus narrowly on single acute decisions, providing minimal guidance for decisions related to chronic disease management over time. Few models account for the role of the child in the decision-making process, despite their ongoing development. Therefore, we propose a model of shared decision-making in the context of managing chronic illness in children that recognizes all actors and can support both the design of clinical care and research. This model—The SDM Learning Loop Model—highlights the dynamic iterative nature of exchanges between and among the clinical team and the parent-child dyad and recognizes the child as the center of each decision-making cycle. The model accounts for key practice, family, experiential, and emotional contexts influencing the decision-making encounter. In this model, change in child health status and developmental capacity resulting from a given cycle’s care plan will directly influence the relationship between clinician and parent-child dyad (eg, mutual trust, attunement) and impact each party’s engagement in the next round of decision-making. The relationship between experience and outcome stimulates learning. DISCUSSION/SIGNIFICANCE OF IMPACT: Our proposed SDM Learning Loop Model suggests that increasing the shared nature of decision making is not only likely to optimize care planning, but creates “buy-in” that can both reinforce the impact of positive outcomes, and moderate the negative impact on relationships when the outcome is other than desired. We hypothesize that this model can guide care planning and shape research to the benefit of both clinical outcomes and clinician-family relationships. Future work should focus on the development and validation of measures to account for the experiential and emotional contexts in which such decisions are made, and the outcomes of care in this population.