Problem formulation and organizational decision-making : biases and assumptions underlying alternative models of strategic problem formulation

Bibliography: p. 20-25

A Sensitivity Matrix Methodology for Inverse Problem Formulation

We propose an algorithm to select parameter subset combinations that can be estimated using an ordinary least-squares (OLS) inverse problem formulation with a given data set. First, the algorithm selects the parameter combinations that correspond to sensitivity matrices with full rank. Second, the algorithm involves uncertainty quantification by using the inverse of the Fisher Information Matrix. Nominal values of parameters are used to construct synthetic data sets, and explore the effects of removing certain parameters from those to be estimated using OLS procedures. We quantify these effects in a score for a vector parameter defined using the norm of the vector of standard errors for components of estimates divided by the estimates. In some cases the method leads to reduction of the standard error for a parameter to less than 1% of the estimate

Problem Formulation and Fairness

Formulating data science problems is an uncertain and difficult process. It requires various forms of discretionary work to translate high-level objectives or strategic goals into tractable problems, necessitating, among other things, the identification of appropriate target variables and proxies. While these choices are rarely self-evident, normative assessments of data science projects often take them for granted, even though different translations can raise profoundly different ethical concerns. Whether we consider a data science project fair often has as much to do with the formulation of the problem as any property of the resulting model. Building on six months of ethnographic fieldwork with a corporate data science team---and channeling ideas from sociology and history of science, critical data studies, and early writing on knowledge discovery in databases---we describe the complex set of actors and activities involved in problem formulation. Our research demonstrates that the specification and operationalization of the problem are always negotiated and elastic, and rarely worked out with explicit normative considerations in mind. In so doing, we show that careful accounts of everyday data science work can help us better understand how and why data science problems are posed in certain ways---and why specific formulations prevail in practice, even in the face of what might seem like normatively preferable alternatives. We conclude by discussing the implications of our findings, arguing that effective normative interventions will require attending to the practical work of problem formulation.Comment: Conference on Fairness, Accountability, and Transparency (FAT* '19), January 29-31, 2019, Atlanta, GA, US

Problem-formulation in a South African organization. Executive summary

Complex Problem Solving is an area of cognitive science that has received a good amount of attention, but theories in the field have not progressed accordingly. In general, research of problem solving has focussed on identifying preferable methods rather than on what happens when human beings confront problems in an organizational context Queseda, Kirtsch and Gomez (2005) Existing literature recognises that most organizational problems are ill-defined. Some problems can become well-defined whereas others are and remain ill-structured. For problems that can become well-defined, failure to pay attention to the area of problem definition has the potential to jeopardise the effectiveness of problem-formulation and thus the entire problem solving activity. Problem defining, a fundamental part of the problem-formulation process, is seen as the best defence against a Type III Error (trying to solve the wrong problem). Existing literature addresses possible processes for problem-formulation and recognises the importance of applying problem domain knowledge within them. However, inadequate attention is given to the possible circumstances that, within an organization, the participants do not know enough about the problem domain and do not recognise the importance of applying adequate problem domain knowledge or experience to the problem-formulation process. A case study is conducted into exactly these circumstances as they occurred and were successfully addressed within Eskom Holdings Ltd (Eskom), the national electricity utility in South Africa. The case study is a fundamental part of this research project, which explores the gap in the existing body of knowledge related to the circumstances described above and specifically to problems that can become well-defined, and provides the basis for the innovation developed herein that addresses that gap

A geometrical formulation of the μ-lower bound problem

A new problem formulation for the structured singular value μ in the case of purely real (possibly repeated) uncertainties is presented. The approach is based on a geometrical interpretation of the singularity constraint arising in the μ lower bound problem. An interesting feature of this problem formulation is that the resulting parametric search space is independent of the number of times any parameter is repeated in the structured uncertainty matrix. A corresponding lower bound algorithm combining randomisation and optimisation methods is developed, and some probabilistic performance guarantees are derived. The potential usefulness of the proposed approach is demonstrated on two high-order real μ analysis problems from the aerospace and systems biology literature

Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution

With the increasing share of renewable and distributed generation in electrical distribution systems, Active Network Management (ANM) becomes a valuable option for a distribution system operator to operate his system in a secure and cost-effective way without relying solely on network reinforcement. ANM strategies are short-term policies that control the power injected by generators and/or taken off by loads in order to avoid congestion or voltage issues. Advanced ANM strategies imply that the system operator has to solve large-scale optimal sequential decision-making problems under uncertainty. For example, decisions taken at a given moment constrain the future decisions that can be taken and uncertainty must be explicitly accounted for because neither demand nor generation can be accurately forecasted. We first formulate the ANM problem, which in addition to be sequential and uncertain, has a nonlinear nature stemming from the power flow equations and a discrete nature arising from the activation of power modulation signals. This ANM problem is then cast as a stochastic mixed-integer nonlinear program, as well as second-order cone and linear counterparts, for which we provide quantitative results using state of the art solvers and perform a sensitivity analysis over the size of the system, the amount of available flexibility, and the number of scenarios considered in the deterministic equivalent of the stochastic program. To foster further research on this problem, we make available at http://www.montefiore.ulg.ac.be/~anm/ three test beds based on distribution networks of 5, 33, and 77 buses. These test beds contain a simulator of the distribution system, with stochastic models for the generation and consumption devices, and callbacks to implement and test various ANM strategies

Engaged Problem Formulation in IS Research

“Is this the problem?”: the question that haunts many information systems (IS) researchers when they pursue work relevant to both practice and research. Nevertheless, a deliberate answer to this question requires more than simply asking the involved IS practitioners. Deliberately formulating problems requires a more substantial engagement with the different stakeholders, especially when their problems are ill structured and situated in complex organizational settings. On this basis, we present an engaged approach to formulating IS problems with, not for, IS practitioners. We have come to understand engaged problem formulation as joint researching and as the defining of contemporary and complex problems by researchers and those practitioners who experience and know these problems. We used this approach in investigating IS management in Danish municipalities. In this paper, we present the approach to formulating problems in an engaged way. We discuss it in relation to ideas and assumptions that underpin engaged scholarship, and we discuss the implications for IS action research, design science research, and mixed approaches

Flow Measurement: An Inverse Problem Formulation

This paper proposes a new mathematical formulation for flow measurement based on the inverse source problem for wave equations with partial boundary measurement. Inspired by the design of acoustic Doppler current profilers (ADCPs), we formulate an inverse source problem that can recover the flow field from the observation data on a few boundary receivers. To our knowledge, this is the first mathematical model of flow measurement using partial differential equations. This model is proved well-posed, and the corresponding algorithm is derived to compute the velocity field efficiently. Extensive numerical simulations are performed to demonstrate the accuracy and robustness of our model. Our formulation is capable of simulating a variety of practical measurement scenarios