Hurricane Katrina, the Crisis of Leadership, and Chaos Management: Time for Trying the ‘Surprise Management Theory in Action’

Four years have gone by since the historic Hurricane Katrina hit and drowned the city of New Orleans and caused a massive crisis of, and a global case of grand failure in, governance, leadership, and public management. Advancing on an earlier work published in Public Administration Review (Farazmand 2007), in which a global case of grand failure was established with several lessons drawn for future crisis management, this article argues further for developing and applying a theory of ‘surprise management’ to manage future crises and chaotic situations. Crises are borne out of natural and human made disasters, catastrophes, revolutions, and rapidly changing emergencies. Surprise management is the best approach to managing or coping with crises and crisis driven emergencies.     

The Mirrlees Approach to Mechanism Design with Renegotiation (with Applications to Hold‐up and Risk Sharing)

The paper studies the implementation problem, first analyzed by Maskin and Moore (1999), in which two agents observe an unverifiable state of nature and may renegotiate inefficient outcomes following play of the mechanism. We develop a first‐order approach to characterizing the set of implementable utility mappings in this problem, paralleling Mirrlees's (1971) first‐order analysis of standard mechanism design problems. We use this characterization to study optimal contracting in hold‐up and risk‐sharing models. In particular, we examine when the contracting parties can optimally restrict attention to simple contracts, such as noncontingent contracts and option contracts (where only one agent sends a message).     

Coordinating Supply Chains via Advance‐Order Discounts,Minimum Order Quantities,and Delegations

To avoid inventory risks, manufacturers often place rush orders with suppliers only after they receive firm orders from their customers (retailers). Rush orders are costly to both parties because the supplier incurs higher production costs. We consider a situation where the supplier's production cost is reduced if the manufacturer can place some of its order in advance. In addition to the rush order contract with a pre‐established price, we examine whether the supplier should offer advance‐order discounts to encourage the manufacturer to place a portion of its order in advance, even though the manufacturer incurs some inventory risk. While the advance‐order discount contract is Pareto‐improving, our analysis shows that the discount contract cannot coordinate the supply chain. However, if the supplier imposes a pre‐specified minimum order quantity requirement as a qualifier for the manufacturer to receive the advance‐order discount, then such a combined contract can coordinate the supply chain. Furthermore, the combined contract enables the supplier to attain the first‐best solution. We also explore a delegation contract that either party could propose. Under this contract, the manufacturer delegates the ordering and salvaging activities to the supplier in return for a discounted price on all units procured. We find the delegation contract coordinates the supply chain and is Pareto‐improving. We extend our analysis to a setting where the suppliers capacity is limited for advance production but unlimited for rush orders. Our structural results obtained for the one‐supplier‐one‐manufacturer case continue to hold when we have two manufacturers.     

AMBIVALENT INVESTMENT AND THE HOLD‐UP PROBLEM

In this paper we add to the foundations of incomplete contracting literature. We study the hold‐up problem with ambivalent investment, where investment benefits the investing party if ex post the right decision is undertaken but harms the investing party if the wrong decision is made. In this context, we show that the power of contracts to provide investment incentives depends on three factors: the commitment value of contracts, the amount of quasirents that the investing party can expect in the case of out‐of‐contract renegotiation, and the degree of ambivalence of investment. First, contracts provide first‐best investment incentives when parties can commit to a contract regardless of the type of investment. Second, with sufficiently ambivalent investment, if parties cannot commit not to renegotiate a contract and if the investing party's bargaining power is intermediate, contracts cannot improve investment incentives above those provided by no contract. In contrast, a simple buyer or seller option contract is optimal when the investing party's bargaining power is extreme. (JEL: D23, K12, L22)     

Optimal algorithms for online time series search and one-way trading with interrelated prices

The basic models of online time series search and one-way trading are introduced by El-Yaniv et al. in Algorithmica 30(1), 101–139 (2001) where it is assumed that the prices are bounded within interval [m,M] (0<m<M). In this paper, we consider another case where every two consecutive prices are interrelated, that is, the variation range of each price depends on its preceding price. We present optimal deterministic online algorithms for the two problems, respectively. According to one conclusion in Algorithmica 30(1), 101–139 (2001), we further point out that for the case we considered, an optimal deterministic algorithm for the one-way trading problem can be regarded as an optimal randomized one for the time series search problem, and randomization is useless for the one-way trading problem.     

Using Real‐Time Decision Tools to Improve Distributed Decision‐Making Capabilities in High‐Magnitude Crisis Situations*

Multi‐organizational collaborative decision making in high‐magnitude crisis situations requires real‐time information sharing and dynamic modeling for effective response. Information technology (IT) based decision support tools can play a key role in facilitating such effective response. We explore one promising class of decision support tools based on machine learning, known as support vector machines (SVM), which have the capability to dynamically model and analyze decision processes. To examine this capability, we use a case study with a design science approach to evaluate improved decision‐making effectiveness of an SVM algorithm in an agent‐based simulation experimental environment. Testing and evaluation of real‐time decision support tools in simulated environments provides an opportunity to assess their value under various dynamic conditions. Decision making in high‐magnitude crisis situations involves multiple different patterns of behavior, requiring the development, application, and evaluation of different models. Therefore, we employ a multistage linear support vector machine (MLSVM) algorithm that permits partitioning decision maker response into behavioral subsets, which can then individually model and examine their diverse patterns of response behavior. The results of our case study indicate that our MLSVM is clearly superior to both single stage SVMs and traditional approaches such as linear and quadratic discriminant analysis for understanding and predicting behavior. We conclude that machine learning algorithms show promise for quickly assessing response strategy behavior and for providing the capability to share information with decision makers in multi‐organizational collaborative environments, thus supporting more effective decision making in such contexts.     

THE FAILURE TO PREDICT THE GREAT RECESSION—A VIEW THROUGH THE ROLE OF CREDIT

Much has been written about why economists failed to predict the latest crisis. Reading the literature, it seems that this crisis was so obvious that economists must have been blind not to see it coming. We approach this failure by looking at one of the key variables in this analysis, the evolution of credit. We compare the conclusions reached in the recent literature with those that could have been drawn from an ex‐ante analysis. We show that the effect of credit on the business cycle cannot be exploited from a policymaker's point of view.     

A tighter formulation of the <Emphasis Type="Italic">p</Emphasis>-median problem

Given a set of clients and a set of potential sites for facilities, the p-median problem consists of opening a set of p sites and assigning each client to the closest open facility to it. It can be viewed as a variation of the uncapacitated facility location problem. We propose a new formulation of this problem by a mixed integer linear problem. We show that this formulation, while it has the same value by LP-relaxation, can be much more efficient than two previous formulations. The computational experiment performed on two sets of benchmark instances has showed that the efficiency of the standard branch-and-cut algorithm has been significantly improved. Finally, we explore the structure of the new formulation in order to derive reduction rules and to accelerate the LP-relaxation resolution.     

Myopic Analysis for Multi‐Echelon Inventory Systems with Batch Ordering and Nonstationary/Time‐Correlated Demands

We provide an exact myopic analysis for an N‐stage serial inventory system with batch ordering, linear ordering costs, and nonstationary demands under a finite planning horizon. We characterize the optimality conditions of the myopic nested batching newsvendor (NBN) policy and the myopic independent batching newsvendor (IBN) policy, which is a single‐stage approximation. We show that echelon reorder levels under the NBN policy are upper bounds of the counterparts under both the optimal policy and the IBN policy. In particular, we find that the IBN policy has bounded deviations from the optimal policy. We further extend our results to systems with martingale model of forecast evolution (MMFE) and advance demand information. Moreover, we provide a recursive computing procedure and optimality conditions for both heuristics which dramatically reduces computational complexity. We also find that the NBN problem under the MMFE faced by one stage has one more dimension for the forecast demand than the one faced by its downstream stage and that the NBN policy is optimal for systems with advance demand information and stationary problem data. Numerical studies demonstrate that the IBN policy outperforms on average the NBN policy over all tested instances when their optimality conditions are violated.     

Crisis Communication Post Katrina: What are we Learning?

Hurricane Katrina and post-Katrina audits are an on-going project for public affairs scholarship. Much has been written but critical frameworks of analysis are minimal. Crisis communication taxonomies are rich and fruitful in understanding the framing of calamitous events. In revisiting a previous mapping of crisis communication vulnerability points in the Katrina tragedy, the authors provide a further “vulnerability audit” of crisis-communication capabilities within a changing political/administrative ontology—one pointing to the privatization of crises/disasters within the Neo-liberal state. Lessons about crisis communication according to the earlier four conceptual lenses (Garnett and Kouzmin 2007) are supported by more recent developments and scholarship.     

Optimal algorithms for uncovering synteny problem

The syntenic distance between two genomes is the minimum number of fusions, fissions, and translocations that can transform one genome to the other, ignoring the gene order within chromosomes. As the problem is NP-hard in general, some particular classes of synteny instances, such as linear synteny, exact synteny and nested synteny, are examined in the literature. In this paper, we propose a new special class of synteny instances, called uncovering synteny. We first present a polynomial time algorithm to solve the connected case of uncovering synteny optimally. By performing only intra-component moves, we then solve the unconnected case of uncovering synteny. We will further calculate the diameters of connected and unconnected uncovering synteny, respectively.     

Combinatorial Optimization in Real-Time Scheduling: Theory and Algorithms

Real-time computer systems are essential for many applications, such as robot control, avionics, medical instrumentation, manufacturing, etc. The correctness of the system depends on the temporal correctness as well as the functional correctness of the task executions. In order to assure temporal correctness it is necessary that the resources be scheduled to meet the temporal requirements of applications. When we consider the problem of nonpreemptive scheduling of a set of tasks in a processor for which no feasible solution exists, some tasks may have to be rejected so that a schedule can be generated for the rest. In this paper, we consider the problem of generating an optimal schedule such that the number of rejected tasks is minimized, and then the finish time is minimized for the accepted tasks. We propose to use an analytic approach to solve this problem. We first discuss the super sequence based technique which was originally proposed for reducing the search space in testing the feasibility of a task set. Then we show by the Conformation theorem that the super sequence constructed from the task set also provides a valid and reduced search space for the optimization problem. While the complexity of our scheduling algorithm in the worst case remains exponential, our simulation results show that the cost is reasonable for the average case.     

Mitigating the U.S. Drug Shortages Through Pareto‐Improving Contracts

Drug shortages have been a major challenge facing the US pharmaceutical industry and government in recent years. Although the problem has drawn tremendous attention from the government and media, limited academic research has been devoted to this problem, and few solutions have been proposed based on rigorous research. This study addresses the drug shortage problem from a supply chain perspective, a key aspect missing in the literature, and proposes to mitigate shortages through drug purchase contracts. By modeling the drug supply chain, we capture the objectives of various supply chain parties, and investigate Pareto‐improving contracts that mitigate drug shortages, improve drug manufacturer's and group purchasing organization (GPO)'s profits, and cut government spending and healthcare providers’ costs. We explore structural properties of key supply chain decisions and the Pareto‐improving contracts, and conduct scenario analysis with realistic industry data to evaluate shortage mitigation solutions. Our analysis shows that increasing drug prices only, a solution advocated by many, is not very effective in shortage mitigation. Price increases must be paired with strengthened failure‐to‐supply clauses (called the IPS approach) to achieve consistent and significant shortage reduction as well as Pareto improvement. Across all scenarios tested, a 30% price increase under IPS can lead to a minimum, average, and maximum shortage reduction of 25%, 53%, and 70%, respectively. Our analysis also shows the impacts of IPS on different parties in the supply chain and the impacts of various model parameters on shortage mitigation. The IPS approach rewards reliability of drug supply, which is in line with the FDA's strategic plan to reward quality, but is easier to achieve in this regulation‐based industry. Interactions with the government and industry practitioners indicate that IPS also challenges the current mindset in pharmaceutical contracting.     

Misplaced Inventory and Radio‐Frequency Identification (RFID) Technology: Information and Coordination

Misplaced inventory is a major operational problem in many supply chains. Radio‐frequency identification (RFID) technology has been publicized as a promising solution for the misplaced inventory. Adoption of this technology has a fixed cost and variable cost of implementation, which can cause incentive issues in the supply chain. In this paper, we consider a supply chain under misplacement of inventory subject to uncertain demand. We study both centralized and decentralized cases and identify the conditions to coordinate the supply chain under implementation of RFID. We show that the incentives of the parties for investing in the technology are not perfectly aligned in the existence of the fixed cost of investment. Based on the relative payments of the parties for the fixed cost of investment, the incentives to adopt RFID can be characterized into regions, where we observe only one party or two parties benefiting from the technology when the tag price falls in a region specified in the paper. We further establish the effects of changes in mean and variance of a uniform demand on the incentives for investing in RFID and find that the incentives of the firms may indeed decrease as demand becomes more variable.     

Integrating Risk and Resilience Approaches to Catastrophe Management in Engineering Systems

Recent natural and man‐made catastrophes, such as the Fukushima nuclear power plant, flooding caused by Hurricane Katrina, the Deepwater Horizon oil spill, the Haiti earthquake, and the mortgage derivatives crisis, have renewed interest in the concept of resilience, especially as it relates to complex systems vulnerable to multiple or cascading failures. Although the meaning of resilience is contested in different contexts, in general resilience is understood to mean the capacity to adapt to changing conditions without catastrophic loss of form or function. In the context of engineering systems, this has sometimes been interpreted as the probability that system conditions might exceed an irrevocable tipping point. However, we argue that this approach improperly conflates resilience and risk perspectives by expressing resilience exclusively in risk terms. In contrast, we describe resilience as an emergent property of what an engineering system does, rather than a static property the system has. Therefore, resilience cannot be measured at the systems scale solely from examination of component parts. Instead, resilience is better understood as the outcome of a recursive process that includes: sensing, anticipation, learning, and adaptation. In this approach, resilience analysis can be understood as differentiable from, but complementary to, risk analysis, with important implications for the adaptive management of complex, coupled engineering systems. Management of the 2011 flooding in the Mississippi River Basin is discussed as an example of the successes and challenges of resilience‐based management of complex natural systems that have been extensively altered by engineered structures.     

Batch-Processing Scheduling with Setup Times

The problem is to minimize the total weighted completion time on a single batch-processing machine with setup times. The machine can process a batch of at most B jobs at one time, and the processing time of a batch is given by the longest processing time among the jobs in the batch. The setup time of a batch is given by the largest setup time among the jobs in the batch. This batch-processing problem reduces to the ordinary uni-processor scheduling problem when B = 1. In this paper we focus on the extreme case of B = +, i.e. a batch can contain any number of jobs. We present in this paper a polynomial-time approximation algorithm for the problem with a performance guarantee of 2. We further show that a special case of the problem can be solved in polynomial time.     

Developing a Human‐centred and Science‐based Approach to Design: The Knowledge Management Platform Project

The gap between theory and practice is a persistent problem in management and organization research. In this respect, several scholars have suggested that ‘design’ is an ideal‐typical form of mode 2 knowledge production. Design research develops knowledge in the service of action and problem solving in organizational settings. In this paper, we connect two perspectives on design that can be considered to be largely complementary but have hardly been combined and integrated in practice: science‐based design drawing on design propositions grounded in research and human‐centred design emphasizing an active and systematic participation by users and other stakeholders. An integrated approach that builds on both perspectives is developed and subsequently applied to designing and developing a portal for mapping competencies in an information technology cluster. This project involves creating problem awareness and articulating design propositions as well as developing scenarios of use, experimenting with prototypes, and organizational transformation. As such, this methodology addresses the dual challenge of rigour and relevance by producing both scientific and practical knowledge.     

Alignments with non-overlapping moves,inversions and tandem duplications in <Emphasis Type="Italic">O</Emphasis>(<Emphasis Type="Italic">n</Emphasis><Superscript>4</Superscript>) time

Sequence alignment is a central problem in bioinformatics. The classical dynamic programming algorithm aligns two sequences by optimizing over possible insertions, deletions and substitutions. However, other evolutionary events can be observed, such as inversions, tandem duplications or moves (transpositions). It has been established that the extension of the problem to move operations is NP-complete. Previous work has shown that an extension restricted to non-overlapping inversions can be solved in O(n ³) with a restricted scoring scheme. In this paper, we show that the alignment problem extended to non-overlapping moves can be solved in O(n ⁵) for general scoring schemes, O(n ⁴log n) for concave scoring schemes and O(n ⁴) for restricted scoring schemes. Furthermore, we show that the alignment problem extended to non-overlapping moves, inversions and tandem duplications can be solved with the same time complexities. Finally, an example of an alignment with non-overlapping moves is provided. A preliminary version of this paper appeared in the Proceedings of COCOON 2007, LNCS, vol. 4598, pp. 151–164.     

Data aggregation for <Emphasis Type="Italic">p</Emphasis>-median problems

In this paper, we use a pseudo-Boolean formulation of the p-median problem and using data aggregation, provide a compact representation of p-median problem instances. We provide computational results to demonstrate this compactification in benchmark instances. We then use our representation to explain why some p-median problem instances are more difficult to solve to optimality than other instances of the same size. We also derive a preprocessing rule based on our formulation, and describe equivalent p-median problem instances, which are identical sized instances which are guaranteed to have identical optimal solutions.     

Clustering Binary Oligonucleotide Fingerprint Vectors for DNA Clone Classification Analysis

We considered the problem of clustering binarized oligonucleotide fingerprints that attempts to identify clusters. Oligonucleotide fingerprinting is a powerful DNA array based method to characterize cDNA and rRNA libraries and has many applications including gene expression profiling and DNA clone classification. DNA clone classification is the main application for the problem considered in this paper. Most of the existing approaches for clustering use normalized real intensity values and thus do not treat positive and negative hybridization signals equally. This is demonstrated in a series of recent publications where a discrete approach typically useful in the classification of microbial rRNA clones has been proposed. In the discrete approach, hybridization intensities are normalized and thresholds are set such that a value of 1 represents hybridization, a value of 0 represents no hybridization, and an N represents unknown, which is also called a missing value. A combinatorial optimization problem is then formulated attempting to cluster the fingerprints and resolve the missing values simultaneously. It has been examined that missing values cause much difficulty in clustering analysis and most clustering methods are very sensitive to them. In this paper, we turned a little back to the traditional clustering problem, which takes in no missing values but with the revised goal to stabilize the number of clusters and maintain the clustering quality. We adopted the binarizing scheme used in the discrete approach as it is shown to be typically useful for the clone classifications. We formulated such a problem into another combinatorial optimization problem. The computational complexity of this new clustering problem and its relationships to the discrete approach and the traditional clustering problem were studied. We have designed an exact algorithm for the new clustering problem, which is an A* search algorithm for finding a minimum number of clusters. The experimental results on two commonly tested real datasets demonstrated that the A* search algorithm runs fast and performs better than some popular hierarchical clustering methods, in terms of separating clones that have different characteristics with respect to the given oligonucleotide probes.Supported by NSERC and CFI.Supported by NSERC.Supported partially by NSERC, CFI, and NNSF Grant 60373012.