Interdependent Decisions Research Articles

A joint model of residential mobility and location choice decisions

ABSTRACT Residential relocation is a process involving the interdependent decisions of mobility (i.e. the decision to move) and location choice. Location choice is a discrete choice decision, whereas mobility has a continuous time dimension. Since households relocate along their life course for different reasons such as the birth of a child, these reasons could influence the duration of stay as competing events. This paper develops a joint model for mobility and location choice decisions. Data come from a retrospective survey conducted in the Central Okanagan of British Columbia, Canada. A reason-based competing hazard model is developed for the mobility component of the joint model. For the location choice component, a latent segmentation-based logit model is developed to capture unobserved heterogeneity. The influence of location utility on mobility is addressed through log sum parameters. The model confirms the effects of life-cycle events, parcel, socio-demographic, land use, transportation, neighbourhood and accessibility characteristics. The findings reveal that households relocating for the occurrence of life-cycle events are active in the housing market following the birth of a child, marriage and vehicle purchase. Relocation to live in a desirable neighbourhood is found to delay the move for the reason of living closer to activity points. Results also indicate that duration is likely to be shorter with the increase in expected utility from the following location. Regarding the location choice, preferences for dwellings closer to schools are evident. In the case of heterogeneity, it is observed that urban dwellers are more likely to prefer locations closer to the workplace with the addition of jobs in the household. In contrast, suburban dwellers are more likely to prefer residing far from the workplace. The developed joint model has been included in an integrated urban model, currently under development at the University of British Columbia.

Iterative Trajectory Planning and Resource Allocation for UAV-Assisted Emergency Communication with User Dynamics

The demand for air-to-ground communication has surged in recent years, underscoring the significance of unmanned aerial vehicles (UAVs) in enhancing mobile communication, particularly in emergency scenarios due to their deployment efficiency and flexibility. In situations such as emergency cases, UAVs can function as efficient temporary aerial base stations and enhance communication quality in instances where terrestrial base stations are incapacitated. Trajectory planning and resource allocation of UAVs continue to be vital techniques, while a relatively limited number of algorithms account for the dynamics of ground users. This paper focuses on emergency communication scenarios such as earthquakes, proposing an innovative path planning and resource allocation algorithm. The algorithm leverages a multi-stage subtask iteration approach, inspired by the block coordinate descent technique, to address the challenges presented in such critical environments. In this study, we establish an air-to-ground communication model, subsequently devising a strategy for user dynamics. This is followed by the introduction of a joint scheduling process for path and resource allocation, named ISATR (iterative scheduling algorithm of trajectory and resource). This process encompasses highly interdependent decision variables, such as location, bandwidth, and power resources. For mobile ground users, we employ the cellular automata (CA) method to forecast the evacuation trajectory. This algorithm successfully maintains data communication in the emergency-stricken area and enhances the communication quality through bandwidth division and power control which varies with time. The effectiveness of our algorithm is validated by evaluating the average throughput with different parameters in various simulation conditions and by using several heuristic methods as a contrast.

Learning to Resolve Social Dilemmas: A Survey

Social dilemmas are situations of inter-dependent decision making in which individual rationality can lead to outcomes with poor social qualities. The ubiquity of social dilemmas in social, biological, and computational systems has generated substantial research across these diverse disciplines into the study of mechanisms for avoiding deficient outcomes by promoting and maintaining mutual cooperation. Much of this research is focused on studying how individuals faced with a dilemma can learn to cooperate by adapting their behaviours according to their past experience. In particular, three types of learning approaches have been studied: evolutionary game-theoretic learning, reinforcement learning, and best-response learning. This article is a comprehensive integrated survey of these learning approaches in the context of dilemma games. We formally introduce dilemma games and their inherent challenges. We then outline the three learning approaches and, for each approach, provide a survey of the solutions proposed for dilemma resolution. Finally, we provide a comparative summary and discuss directions in which further research is needed.

Incentive-based coordination for scheduled delivery in prefab construction

An increasing number of projects are adopting prefabrication to economize on time, labor, and materials in fixed-position layout operations, such as construction, ship building, and aircraft manufacturing. In such contexts, independent contractor and fabricator make interdependent decisions, which calls for prudent supply chain management because performance relies on coordination between their decisions. Many studies have developed integrated systems and propose various algorithms for scheduling efficiency and reliability. Nevertheless, they pay scant attention to conflicting interests amongst independent partners, which may result in subpar performance not only for the supplier but for the contractor as well. Coordination of conflicting interests has been extensively studied in economics and supply chain management; yet, those studies focus on order-quantity decisions under demand uncertainty for profit maximization, while managers in fixed-position operations are more concerned about delivery decisions under scheduling uncertainty for cost minimization. We consider the case of construction and explore a contractual scheme that aligns the agents’ decisions for coordination in a construction supply chain. Specifically, we propose a supplier rebate for coordination: the supplier grants a rebate if the contractor accepts the shipment in accordance with the delivery schedule that the contractor initially chose. We show that the optimal rebate fully coordinates the supply chain to minimize the joint supply chain costs. Thus, both the contractor and supplier benefit from the coordination by negotiating a mutually acceptable way to allocate the savings in joint costs between them. We further show that the rebate motivates the contractor to enhance its work scheduling.

Configuring mobile app update strategy for growth: An empirical analysis of a landscape search model

PurposeThis study attempts to discover effective strategies for mobile commerce applications (apps) to grow their consumer base by releasing app strategic updates. Drawing on the landscape search model from strategy research, this study conceptualizes mobile app update strategy as three interdependent decisions, i.e. what business elements are changed in an app strategic update, how substantial the changes are and when strategic updates are released relative to the competitive environment.Design/methodology/approachUsing a field data set of 1,500 strategic updates of seven rival apps in the mobile travel market, this study integrated fuzzy set qualitative comparative analysis (fsQCA) with econometric analysis to analyze how app strategic update decisions interdependently influence app performance.FindingsThis study identified three effective and one ineffective mobile app update strategies from the mixed-method analysis, which verified the complex interdependency of app strategic update decisions. A general takeaway from these strategies is that a complex strategy problem on the mobile platform must be solved with respect to the constraints and capabilities of mobile technology.Originality/valueThis study moves beyond a linear view of the relationship between app update frequency and app performance and provides a holistic view of how and why app strategic update decisions mutually influence one another in their impact on app performance. This work makes contributions by identifying interdependency as a conceptual bridge between strategy and mobile app literature and developing an empirically testable version of the landscape search model.

Causality-Based Fair Multiple Decision by Response Functions

A recent trend of fair machine learning is to build a decision model subjected to causality-based fairness requirements, which concern with the causality between sensitive attributes and decisions. Almost all (if not all) solutions focus on a single fair decision model and assume no hidden confounder to model causal effects in a too simplified way. However, multiple interdependent decision models are actually used and discrimination may transmit among them. The hidden confounder is another inescapable fact and causal effects cannot be computed from observational data in the unidentifiable situation. To address these problems, we propose a method called CMFL (Causality-based Multiple Fairness Learning). CMFL parameterizes the causal model by response-function variables, whose distributions capture the randomness of causal models. CMFL treats each classifier as a soft intervention to infer the post-intervention distribution, and combines the fairness constraints with the classification loss to train multiple decision classifiers. In this way, all classifiers can make approximately fair decisions. Experiments on synthetic and benchmark datasets confirm its effectiveness, the response-function variables can deal with the unidentifiable issue and hidden confounders.

Noise and dynamic decision making

Dynamic decision-making (DDM) is a cognitive skill where real-time interdependent decisions are made in an environment which is changing, for example for emergency services, firefighting, battlefield, and aircraft operations to name a few. These are all situations where moderate noise is present, yet little is known about its effect on DDM. A series of research studies investigated the effect of 75 dBA broadband noise on performance for a simulated water distribution task that requires constant DDM. The effect of the noise on performance was found to be moderated by workload, sex, and task familiarity. The noise was detrimental to females’ performance at low workload but not at high workload. Males were generally unaffected by noise regardless of the workload level. This suggests that an optimum level of arousal or stimulation may trigger individuals’ effort to maintain performance in stressors. Furthermore, there may be some benefit from moderate broadband noise for individuals experiencing high cognitive workload. Not unexpectedly, increased task familiarity, through increased experience and extended task instructions, helped reduce the negative noise effect, which is consistent with Maximal Adaptability Theory.

A Logical Study of Moral Responsibility

AbstractThis paper proposes a logical framework for studying the structure of moral responsibility for outcomes. The analysis incorporates two vital features: an agency condition and a negative condition of an alternative possibility. The logical language allows us to identify and disambiguate seven plausible criteria for moral responsibility. To accommodate interdependent decision contexts, the semantics are given in terms of so-called responsibility games. The logical framework enables us to classify the logical relations between these seven criteria for moral responsibility. Although all seven criteria are logically distinct, I also identify circumstances where the seven criteria locally reduce to only three.

Globalising the study of diffusion: multiple sources and the East African Community

ABSTRACT A growing literature on diffusion shows that ideas, policies and institutions originating in the European Union shape decision-making in other regional organisations (ROs). However, a research programme which claims to examine processes of interdependent decision-making between ROs in general cannot treat a single RO as the ‘default’ source of this diffusion. This paper thus (1) discusses what we term the ‘multiple sources’ problem in diffusion research and (2) presents a model of institutional diffusion, which highlights the important role of local actors in the multiple sources scenario. Based on original data collected via extensive fieldwork in East Africa, we present a case study on the interdependent sources behind the East African Community (EAC)’s establishment in drawing on this model. We find that EAC policymakers selected between, combined and modified several external institutions. These findings highlight local actors’ capacity for institutional innovation.

Robotized sorting systems: Large-scale scheduling under real-time conditions with limited lookahead

To meet the expectations of demanding customers, there is a trend toward warehouse automation, especially in large e-commerce distribution centers. In this context, this paper considers robotized sorting systems, where autonomous mobile robots are applied to automatically sort products after picking. The robots load individual pieces of stock keeping units (SKUs) at a loading station, drive to the collection points temporarily associated with customer orders, and autonomously release them, e.g., by tilting a tray mounted on top of each robot. In these systems, a huge number of products approach the loading station with an interarrival time of very few seconds. Hence, we have a very challenging real-time environment for the following decisions: First, since pieces of the same SKU are interchangeable among orders with a demand for this specific SKU, we must assign pieces to suitable orders. Furthermore, each order must be temporarily assigned to a collection point. Finally, we must assign robots to transport jobs. These interdependent decisions become even more involved, since we (typically) do not possess complete knowledge on the arrival sequence but have merely a restricted lookahead of the next approaching products. We show that even in such a fierce environment sophisticated optimization, based on a novel two-step multiple-scenario approach applied under real-time conditions, can be a serviceable tool to significantly improve the sortation throughput. With our approach a limited-lookahead system is shown to almost reach the performance of a (hypothetical) system with complete knowledge on the approaching products.

An adaptive large neighborhood search method for rebalancing free-floating electric vehicle sharing systems

Free-floating electric vehicle sharing systems allow users to pick up an available electric vehicle (EV) and return it to any permissible parking location within a service area. Such service flexibility can drive a severe spatial imbalance between vehicle availability and trip demands. Hence, it is an important part of their operations to relocate the EV fleet to meet the next day’s demand with sufficient battery levels. This relocation operation involves a complicated routing problem for a fleet of shuttles to transport the staff drivers who recharge, if necessary, and relocate the EVs to proper demand locations. Characterized by unique hierarchical and interdependent decisions, the EV relocation and shuttle routing problem poses significant computational challenges for large-scale problems. We devise an efficient algorithm that adapts the Adaptive Large Neighborhood Search (ALNS) metaheuristic framework to overcome such unique challenges. The algorithm is tested on two sets of data: randomly generated data and real-world EV-sharing usage data in Amsterdam. The results validate the efficiency and effectiveness of our ALNS algorithm. In addition, the analysis of total operational cost and waiting time percentage provides practical recommendations to decision-makers on choosing the mode of staff transportation (e.g., shuttles vs. personal mobility such as scooters). Lastly, our numerical results also highlight the usefulness of our ALNS method, which is quite flexible to be applied to a dynamic environment where some EV demands are removed or added in the course of EV relocation operations.

Application of HSMAAOA Algorithm in Flood Control Optimal Operation of Reservoir Groups

The joint flood control operation of reservoir groups is a complex engineering problem with a large number of constraints and interdependent decision variables. Its solution has the characteristics of strong constraint, multi-stage, nonlinearity, and high dimension. In order to solve this problem, this paper proposes a hybrid slime mold and arithmetic optimization algorithm (HSMAAOA) combining stochastic reverse learning. Since ancient times, harnessing the Yellow River has been a major event for the Chinese nation to rejuvenate the country and secure the country. Today, flood risk is still the greatest threat to the Yellow River basin. This paper chooses five reservoirs in the middle and lower reaches of the Yellow River as the research object, takes the water level of each reservoir in each period as the decision variable, and takes the peak clipping of Huayuankou control point as the objective to build an optimization model. Then, HSMAAOA is used to solve the problem, and the results are compared with those of the slime mold algorithm (SMA) and particle swarm optimization (PSO). The peak clipping rates of the three algorithms are 52.9% (HSMAAOA), 48.69% (SMA), and 47.55% (PSO), respectively. The results show that the HSMAAOA algorithm is better than other algorithms. This paper provides a new idea to solve the problem of the optimal operation of reservoir flood controls.

Flood Control Optimization of Reservoir Group Based on Improved Sparrow Algorithm (ISSA)

The optimal control problem of reservoir group flood control is a complex, nonlinear, high-dimensional, multi-peak extremum problem with many complex constraints and interdependent decision variables. The traditional algorithm is slow and easily falls into the local optimum when solving the problem of the flood control optimization of reservoir groups. The intelligent algorithm has the characteristics of fast computing speed and strong searching ability, which can make up for the shortcomings of the traditional algorithm. In this study, the improved sparrow algorithm (ISSA) combining Cauchy mutation and reverse learning strategy is used to solve the flood control optimization problem of reservoir groups. This study takes Sanmenxia Reservoir and Xiaolangdi Reservoir on the mainstream of the Yellow River as the research object and Huayuankou as the downstream control point to establish a joint flood control optimization operation model of cascade reservoirs. The results of the improved sparrow algorithm (ISSA), particle swarm optimization (POS) and sparrow algorithm (SSA) are compared and analyzed. The results show that when the improved ISSA algorithm is used to solve the problem, the maximum flood peak flow of the garden entrance control point is 11,676.3 m3, and the peak cutting rate is 48%. The optimization effect is obviously better than the other two algorithms. This study provides a new and effective way to solve the problem of flood control optimization of reservoir groups.

The timing and mode of foreign exit from conflict zones: A behavioral perspective

We examine the timing and mode of firm exits from host-country conflict zones. We argue that timing and mode are interdependent decisions where decision ordering matters, and show that a firm’s prioritizing of either exit timing or mode is dependent on the relative salience of two behavioral stimuli: (1) the firm’s own experience (i.e., its performance shortfall), and (2) the experience of peer firms (i.e., their exits). Using instrumental variables modeling on a sample of 101 Japanese MNE exits from 11 conflict-afflicted countries between 1991 and 2005, we demonstrate that, when mode is prioritized over timing, partial exits tend to occur earlier and whole exits later. However, when timing is prioritized over mode, the decision choices reverse: earlier exits tend to be whole and later exits partial. The outcome of one decision therefore affects that of the other in a unique and predictable manner, such that the ordering of the decisions both produces and precludes strategic choices. Our findings, based on a multidecision problem that has traditionally been treated as a single decision (i.e., foreign exit), delineate expanded boundary conditions for satisficing, as well as reconcile optimizing and satisficing behaviors.

A general Best-Worst method considering interdependency with application to innovation and technology assessment at NASA

The Best-Worst Method (BWM) is a relatively new and popular method for obtaining criteria weights in multi-criteria decision-making. The BWM uses very few comparisons and produces consistent comparisons, leading to more reliable criteria weights. Despite its popularity and reliability, the decision criteria in the BWM are considered independent of one another. However, in most real-world problems, the decision criteria are interdependent. We propose a general form of the BWM (GBWM) to consider the interdependencies and the intensity of the dependencies among the decision criteria in producing relative influence-intensity weights. The new GBWM is simple to understand and implement and delivers reliable results with a high level of consistency in problems with interdependent decision criteria. The results are more reliable than BWM in problems with interdependencies because we consider both their existence and the intensity of the dependencies. In addition, the results are equally or more consistent than BWM because we start with a BWM solution and adjust the BWM solution with a completely consistent vector. We also present a case study for evaluating and prioritizing advanced technology and innovation projects at NASA to demonstrate the applicability of the proposed method.

Disassortative Mixing and Systemic Rational Behaviour: How System Rationality Is Influenced by Topology and Placement in Networked Systems

Interdependent decisionmaking of individuals in social systems can be modelled by games played on complex networks. Players in such systems have bounded rationality, which influences the computation of equilibrium solutions. It has been shown that the ‘system rationality’, which indicates the overall rationality of a network of players, may play a key role in the emergence of scale-free or core-periphery topologies in real-world networks. In this work, we identify optimal topologies and mixing patterns of players which can maximise system rationality. Based on simulation results, we show that irrespective of the placement of nodes with higher rationality, it is the disassortative mixing of node rationality that helps to maximize system rationality in a population. In other words, the findings of this work indicate that the overall rationality of a population may improve when more players with non-similar individual rationality levels interact with each other. We identify particular topologies such as the core-periphery topology, which facilitates the optimisation of system rationality. The findings presented in this work may have useful interpretations and applications in socio-economic systems for maximizing the utility of interactions in a population of strategic players.

Visual Management Requirements to Support Design Planning and Control within Digital Contexts

Difficulties in managing the construction design process are strongly related to its nature, as a large number of interdependent decisions are involved, which need to be made by many different stakeholders, in an environment that has a high degree of uncertainty. Moreover, there is a growing use of digital tools to support design. Traditional communication approaches used in design management only partially comply with the requirements of digital contexts, and new methods and tools are necessary to address these challenges. Visual Management (VM) has the potential to increase process transparency in the design stage, in order to support collaboration and communication and facilitate the transfer of information. However, the literature on the implementation of VM to support design management is still scarce. Moreover, there is limited understanding of the connection between VM and information and communication technologies (ICT). This investigation aims to propose a set of requirements to support VM applications for design planning and control within digital contexts, which can potentially contribute to improving the effectiveness of VM. This set of requirements were initially identified within the literature, considering different fields of knowledge, and then refined in an empirical study that was developed in collaboration with an infrastructure design and consultancy company in the UK. The Design Science Research approach was the methodological approach adopted in this investigation, which involved incremental learning cycles for devising the artefact, carried out in three different projects. The main findings include (i) the definition of a set of VM requirements that are applicable to the context investigated in this research study; (ii) an assessment of the relevance of the requirements for different types of visual practices, hierarchical planning levels, and stakeholders that are involved; (iii) the identification of some current limitations and challenges of implementing digital VM in construction design. From a practical perspective, this set of requirements may guide practitioners and academics in devising and assessing digital VM practices.

Urban consolidation centers and city toll schemes–Investigating the impact of city tolls on transshipment decisions

Around the world, many cities are struggling with the negative effects of urban freight transport, such as congestion and emissions. To mitigate these negative effects, both innovative solutions, such as urban consolidation centers, and regulations, such as city toll schemes and delivery time windows, can be adopted. In this paper, we investigate the impact of city toll regulations on the vehicle routing and transshipment decisions of logistics service providers. We present a mixed-integer linear programming formulation and an adaptive large neighborhood search heuristic that address the interdependent decisions of vehicle routing and the use of urban consolidation centers, taking into account city toll schemes and time window constraints. In this context, we consider heterogeneous fleets, multiple trips per vehicle, and use a multigraph to address the trade-off between fastest and cheapest paths. We validate the adaptive large neighborhood search by comparing it with the mixed-integer linear programming model on test instances with up to 20 customers. To analyze how city toll schemes affect the cost attractiveness of urban consolidation centers and the fleet composition of logistics service providers, we conduct an extensive computational study featuring real-world instances that provide managerial insights for both local administrations and logistics service providers. Our results show that both per-day and per-entrance tolls can be used by local administrations to support the use of urban consolidation centers and that the number of truck entries into urban areas can be reduced. However, our results also indicate that due to the transshipment fee per unit load, the cost attractiveness of using urban consolidation centers is considerably lower for logistics service providers with larger delivery quantities per stop.

Pricing, assortment, and inventory decisions under nested logit demand and equal profit margins

We consider the interdependent decisions on assortment, inventory and pricing of substitutable products that are differentiated by some primary and secondary attributes captured by a nested logit consumer choice. We examine a newsvendor-type setting with several products competing for demand over a single selling season. We assume that all products have equal profit margins. The demand has a multiplicative-additive structure where both variance and coefficient of variation depend on the common profit margin, which adds to the model applicability at the expense of tractability. Under a Taylor series-type approximation, we show that the expected profit is unimodal in the common margin of products in a given assortment. Then, we compare the optimal profit margin to the case under ample inventory, which allows understanding the effect of inventory on pricing. We also study the optimal assortment problem under exogenous pricing. We show that the classic result on the optimality of popular sets holds under tight approximations of the profit function. We finally propose a heuristic for jointly deciding on assortment, pricing, and inventory decisions, which assumes equal profit margins of products, and exploit popular sets only. Our detailed numerical study shows that this equal-margin heuristic produces high quality solutions.

How cognitive issue bracketing affects interdependent decision-making in negotiations

In this article, we extend theorizing on how actors in individual decision-making situations deal with multiple choices (choice bracketing) in an important joint, interdependent decision-making context—that is, negotiations. Based on the insights on choice bracketing, we posit that parties handle the complexity of multi-issue decision-making by cognitively grouping issues into separate subsets (issue bracketing). To systematically investigate this issue-bracketing process and elucidate its effects on interdependent parties' perceptions, behavior, and the quality of their agreements, we made competing propositions as to how parties would deal with varying numbers of negotiation issues: When facing a higher number of issues, parties may (1) bracket few(er) issues into more subsets, thereby resulting in (a) inadvertent scattering of integrative issues across issue subsets, (b) impaired exploration of integrative potential, and (c) a lower quality of joint decision outcomes. In stark contrast, parties could (2) bracket more issues into few(er) subsets, thereby resulting in (a) pooling of integrative issues within issue subsets, (b) facilitated exploration of integrative potential, and (c) higher-quality outcomes. We tested these predictions in one non-interactive and three interactive experiments (Ntotal = 815) using statistical and experimental mediation analyses. Results consistently revealed that negotiators facing more issues bracketed few issues into more subsets rather than more issues into fewer subsets. In turn, this narrow issue bracketing resulted in a scattering effect of the integrative issues and ultimately a lower integrative quality of decision outcomes. Findings are corroborated via an internal meta-analysis, and their theoretical and applied implications are discussed.