Decision-making In Complex Systems Research Articles

Large-scale group hierarchical DEMATEL method with automatic consensus reaching

Decision-making trial and evaluation laboratory (DEMATEL) is widely used because of its ability to effectively analyze nonlinear relationships between factors in complex systems. With the increasing complexity of decision-making problems, large-scale group decision-making (LSGDM) has become the norm. Most existing DEMATEL methods are only suitable for small-scale groups and simple systems. This study, therefore, proposes a large-scale group hierarchical DEMATEL method that considers consensus reaching. The DEMATEL method for LSGDM faces three challenges: large differences in knowledge structures, difficulty coordinating expert opinions, and slow group-consensus convergence. To address these challenges, first, we use hierarchical decomposition to decompose the complex system into simple systems with different levels to reduce the difficulty of decision-making in complex systems. Second, considering the limitations of expert knowledge and experience, we use the basic probability assignment function to extract the opinions of experts at different levels of subsystems and factors. Third, we divide experts into different clusters using K-means clustering to solve the problem of difficult expert-opinion coordination. Fourth, we design two types of consensuses (intrasubgroup and intersubgroup consensus) and an efficient new type of opinion autocorrection mechanism to solve the problem of the slow convergence of intragroup consensus and improve the efficiency of consensus reaching. Finally, we demonstrate the superiority of the proposed method through data analysis and method comparison.

Quantifying and explaining machine learning uncertainty in predictive process monitoring: an operations research perspective

AbstractIn the rapidly evolving landscape of manufacturing, the ability to make accurate predictions is crucial for optimizing processes. This study introduces a novel framework that combines predictive uncertainty with explanatory mechanisms to enhance decision-making in complex systems. The approach leverages Quantile Regression Forests for reliable predictive process monitoring and incorporates Shapley Additive Explanations (SHAP) to identify the drivers of predictive uncertainty. This dual-faceted strategy serves as a valuable tool for domain experts engaged in process planning activities. Supported by a real-world case study involving a medium-sized German manufacturing firm, the article validates the model’s effectiveness through rigorous evaluations, including sensitivity analyses and tests for statistical significance. By seamlessly integrating uncertainty quantification with explainable artificial intelligence, this research makes a novel contribution to the evolving discourse on intelligent decision-making in complex systems.

Configuring systems to be viable in a crisis: The role of intuitive decision-making

Decision-making in complex systems becomes even more challenging when the environment creates volatile, uncertain, complex, and ambiguous conditions that disrupt operations. In these settings, the viable system model (VSM) advocates that delegated autonomy, appropriately calibrated, can help decision-makers deal with disruptions quickly to preserve system viability and performance. However, the delegated authority to act also requires the confidence and knowledge to make effective decisions and, in this vein, we explore the role of intuition as an enabler of autonomy in emergency response systems. Intuition allows decision-makers who confront a novel situation to translate their experience, knowledge, and protocols in creative ways – innovations permitted by their delegated authority. This study contributes to VSM literature by a) demonstrating how VSM's structure and complexity management principles can support the analysis of viability in multi-agency emergency systems and b) using VSM to design a systems model to explain the role of autonomy and intuition in supporting decision-making and complexity management in viable systems. Methodologically, the study uses a multi-stage discovery-oriented approach (DOA) to develop theory, with each stage combining literature, data analysis, and model/theory development and identifying further questions to inform the subsequent stage. Through the DOA, we synthesise literature (e.g. on VSM, complexity management) with seven months of field-based insights (from interviews, workshops, and observation of a live disaster exercise) to develop VSM models. This research makes two contributions to soft operational research (OR) literature: taking a black-box approach to theory development in soft OR to uncover the role of autonomy and intuition in managing complexity and demonstrating DOA as a methodology that can provide fresh insights for behavioural soft OR studies.

Интеллектуальная схема распределения задач с учетом задержек вычислений в Edge-Fog-Cloud – обзор

The huge volume of data produced by IoT procedures needs the processing power and space for storage provided by cloud, edge, and fog computing systems. Each of these ways of computing has benefits as well as drawbacks. Cloud computing improves the storage of information and computational capability while increasing connection delay. Edge computing and fog computing offer similar advantages with decreased latency, but they have restricted storage, capacity, and coverage. Initially, optimization has been employed to overcome the issue of traffic dumping. Conversely, conventional optimization cannot keep up with the tight latency requirements of decision-making in complex systems ranging from milliseconds to sub-seconds. As a result, ML algorithms, particularly reinforcement learning, are gaining popularity since they can swiftly handle offloading issues in dynamic situations involving certain unidentified data. We conduct an analysis of the literature to examine the different techniques utilized to tackle this latency-aware intelligent task offloading issue schemes for cloud, edge, and fog computing. The lessons acquired consequently, from these surveys are then presented in this report. Lastly, we identify some additional avenues for study and problems that must be overcome in order to attain the lowest latency in the task offloading system.

Large-scale group-hierarchical DEMATEL method for complex systems.

Existing Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods are mostly suitable for simple systems with fewer factors, and lack effective integration of expert knowledge and experience from large-scale group populations, resulting in a potential compromise of the quality of the initial direct relation (IDR) matrix. To make DEMATEL better suited for the identification of critical factors in complex systems, this paper proposes a hierarchical DEMATEL method for large-scale group decision-making. Considering the limitations of expert knowledge and experience, a method based on expert consistency network for constructing the expert weight matrix is designed. The expert consistency network is constructed for different elements, and the weights of experts in different elements are determined using the clustering coefficient. Following the principles of the classic DEMATEL method, the steps for identifying key elements in complex systems using the large-scale group-hierarchical DEMATEL method are summarized. To objectively test the effectiveness and superiority of the decision algorithm, the robustness of the algorithm is analyzed in an interference environment. Finally, the superiority of the proposed method and algorithm is verified through a case study, which demonstrating that the proposed decision-making method is suitable for group decision-making in complex systems, with high algorithm stability and low algorithm deviation.

Data envelopment analysis models based on decentralized decision making

Information technologies, such as blockchain, have promoted the development of decentralized decision-making in complex systems and generated many decentralized autonomous organizations. We construct a set of decentralized Data Envelopment Analysis (DEA) models based on the basic decentralized decision mode of “votingmajority”, and we consider the assumptions that DMUs have no prior information about other DMUs’ decisions, and that they do. Then, the process of decision-making for each decision making unit (DMU) can be viewed as a social behavior over multiple time periods. Using a numerical example, we demonstrate that the new DEA models can provide acceptable efficiency results with specific weights for each DMU and can describe the details of the consensus relationship among DMUs. Similar to the market mechanism in reality, the efficiency scores of DMUs within an alliance containing more than half of the participants are overestimated, at the expense of nonallied DMUs. The models in this paper provide a possible path for efficiency measurement based on decentralized decision-making principles in complex systems.

Policy and political perceptions of risk: the challenges to building resilient energy systems.

Decisions relating to energy systems resilience must take into account a range of environmental and societal transitions together with an array of future threats. These must be assessed broadly and systematically but also must consider how risks and vulnerabilities are linked, and that small events can cascade across a system and between systems to escalate into large-scale collapse. Developing resilience involves not only identifying such threats and potential points of failure but acting upon them with an appropriate level of future planning. Decision-making in complex systems is often affected by cognitive biases and runs into contestation and obstacles of politics, policy complexities and differing views of potential futures. This paper discusses the challenges to building resilient systems for the future, given the inevitable biases and differing risk perspectives of decision-makers at all levels that often confound expert analysis. This article is part of the theme issue 'Developing resilient energy systems'.

Personalized next-best action recommendation with multi-party interaction learning for automated decision-making.

Automated next-best action recommendation for each customer in a sequential, dynamic and interactive context has been widely needed in natural, social and business decision-making. Personalized next-best action recommendation must involve past, current and future customer demographics and circumstances (states) and behaviors, long-range sequential interactions between customers and decision-makers, multi-sequence interactions between states, behaviors and actions, and their reactions to their counterpart’s actions. No existing modeling theories and tools, including Markovian decision processes, user and behavior modeling, deep sequential modeling, and personalized sequential recommendation, can quantify such complex decision-making on a personal level. We take a data-driven approach to learn the next-best actions for personalized decision-making by a reinforced coupled recurrent neural network (CRN). CRN represents multiple coupled dynamic sequences of a customer’s historical and current states, responses to decision-makers’ actions, decision rewards to actions, and learns long-term multi-sequence interactions between parties (customer and decision-maker). Next-best actions are then recommended on each customer at a time point to change their state for an optimal decision-making objective. Our study demonstrates the potential of personalized deep learning of multi-sequence interactions and automated dynamic intervention for personalized decision-making in complex systems.

THE BASE INFORMATION TECHNOLOGY OF HYBRID PATTERNS RECOGNITION FOR HETEROGENEOUS DATA PROCESSING IN COMPLEX SYSTEMS

The article is dedicated to creation of basic information technology of hybrid recognition for data processing and decision-making in complex systems. The main emphasis is placed on systems that are characterized by the presence of a large number of heterogeneous signs with different nature of origin. At the same time, the data processing is complicated by the fact that the patterns of a complex system have different information content under different current observation conditions also the manifestations of heterogeneous interference and distortion. There are methods and models for processing heterogeneous data in complex systems, but there is no systematic approach to developing of hybrid patterns recognition information technology. The article is devoted to solving this problem. The developed information technology allows to obtain a classification solution with a priori level of reliability. It based on processing a smaller number of the most informative signs. This creates possibility to decline time complexity of data processing. The processing is based on queuing heterogeneous data and then searching for groups of identical classifications. In the case of obtaining a multi-alternative result, classification results are ranked in order their reliability for further expert evaluation. Basic information technology, that was created, may be use for develop of the applied information technologies of hybrid patterns recognition for consolidate data processing in different complex systems.

МЕНЕДЖМЕНТ И СИСТЕМНЫЙ АНАЛИЗ КАК ОСНОВА ЭФФЕКТИВНОГО УПРАВЛЕНИЯ В ЭКОНОМИКЕ

Any management system is subject to change, and each time requires research and management decisionmaking in accordance with the changes. Decision-making in complex systems is carried out by a technical device or a person and is based on the comparison and evaluation of options for action. The study of decision-making procedures and the appropriate organization of the system is an urgent problem in the creation and operation of complex systems. Making such decisions is impossible without system analysis. This is due to the relevance of the article.

Effectiveness of Leadership Decision-Making in Complex Systems

This paper traverses the domains of leadership and decision-making within various types of systems with different levels of complexity. The article presents some background about both leadership and decision-making, and then explores the concept of leadership decision-making and some of the factors involved. The paper then reviews complex systems and provides examples to differentiate complex systems from other systems. Finally, these strands are brought together with a consideration of leadership decision-making in complex systems and presentation of a framework to assist managers faced with decision-making in complexity, based on data collected in a survey. The aim and practical contribution of this paper is to improve the outcomes of leadership decision-making within complex systems, based upon the findings and on a decision aid model derived from them. The paper, therefore, should help people in real life and leaders within organizational settings to improve their decision-making effectiveness within the ever-increasing range of complex situations which are now widely encountered.

Системный анализ и теоретико-игровой инструментарий взаимодействия экономической, правовой и социальной национальных макросистем

Objective: economic and legal description, formal justification and construction of models of the balanced economic, legal and social macrosystems in order to determine effective strategic decisions under uncertainty.Methods: the theoretical and methodological basis of the study is the synthesis of the provisions and principles of game and management theories, economic and mathematical modeling and the game-theoretical models of decision-making in complex systems developed on their basis, as well as theoretical and methodological approaches of system analysis, economics, sociology, and law.Results: the balance of economic, legal and social systems is determined by the target orientation to the growth of the population’s well-being. The existing mechanisms for achieving this balance do not take into account the interests of all participants of the systems. The paper presents a structural analysis of the interaction and mutual influence of economic, legal and social macrosystems (a complex metasystem); the necessity of transforming the existing approaches to their regulation is shown. The problem of ensuring the systems’ balancing is largely related to the conflict of interests of the parties. Therefore, traditional methods of modeling decision-making processes do not reflect the real socio-economic processes. This problem was solved through game-theory models that contribute to the implementation of the constitutionally established norms which define Russia as a social state. When making strategic decisions in the social sphere, it is proposed to use the concept of the Golden rule of morality as the main economic doctrine, instead of the currently used neoliberal approach, based, among other things, on the concept of Nash equilibrium. The equilibrium models of the complex metasystem by Nash and Berge were formalized, constructed and compared.Scientific novelty: in contrast to the Nash equilibrium (principles of rationality) - the traditional approach used in the construction of equilibrium models, - the paper offers a theoretical justification of the development and construction of equilibrium models and balance sheet equilibrium of economic, legal and social macrosystems by Berge, as well as a formalization of effective strategic decisions and their respective risks.A distinctive feature of the constructed macromodels is the use of the philosophical and moral concept of the Golden rule (the mathematical basis is the balance by Berge) as an economic doctrine for strategic decision-making under uncertainty, and the construction of the so-called guaranteed optimal solutions by Pareto (effective), based, among other things, on the concept of the pair “social guarantee - life risk”. If, in real conditions, one of the macrosystems is economic and is a “generator” of uncertainties, a guaranteed solution is formalized, optimal by Slater (weakly effective).Practical significance: the developed methodology for building the system equilibrium of economic, legal and social macro-systems enables to make decisions socially significant for the population that adequately take into account the peculiarities of the modern socio-economic macro-systems, such as nonstationarity, dysfunctionality, incomplete information, inconsistency, conflict, alternatives, multicriterial character, and risk; and to develop assisting intellectual systems of supporting decision-making based on mathematical models built using the game-theory approach.

How to improve strategic decision-making in complex systems when only qualitative information is available

This paper continues a line of research on improving strategic decisions (SD). The nature of governance issues (complex and uncertain world, lack of information, consideration of qualitative and quantitative information, participation of different actors in the decision making processes, etc.) implies that there is no unique solution or strategy in a complex system. On the contrary, there are a range of alternative strategies, which could lead to different futures (scenarios). Working with the qualitative methodology proposed allows decision makers and social actors to be more aware of the directions their decisions could lead the system; and what the key variables are for the implementation of public policies to achieve the desired (or agreed) scenario. An integrated methodology is discussed, and the process of desertification in the Canary Islands is used to illustrate it.

Models of multi-alternative control and decision-making in complex systems

This paper states the evolutionary approach to constructing models of control and decision-making. The approach is grounded on the most general principles of functioning in biological systems, namely, the principles of modularity, multilevel and multi-alternativeness. And finally, the authors consider some directions of implementing these principles in models of intelligent control systems using neural network technology.

Controlling the order of moves in voting by veto. II. Algorithms for constructing an optimal order of moves

The study of issues concerning collective decision-making in complex systems using voting by veto is continued. A dedicated player is assumed to exist in the collective that forms the order of voting (order of moves) for the other participants. The problem of searching for the control strategy for the order of moves that ensures the win of alternative 1 that is preferable for the dedicated player is studied for the previously unstudied cases when such an order of moves exists. In particular, an algorithm for constructing such an order is proposed for the case when there is another participant for which alternative 1 is higher than the fourth from the end and when there is a group of participants that are relatively well-disposed to alternative 1 (more precisely, this alternative is higher than the third from the end for them). It is shown that the preference order of the alternatives that are better than alternative 1 is of no importance for the problem under consideration. It is proved that the conditions for the participants' preferences that are sufficient for the existence of the desired order of moves (they were obtained in the first part of this paper) are unimprovable.

A review of operations research methods applicable to wildfire management

Across the globe, wildfire-related destruction appears to be worsening despite increased fire suppression expenditure. At the same time, wildfire management is becoming increasingly complicated owing to factors such as an expanding wildland–urban interface, interagency resource sharing and the recognition of the beneficial effects of fire on ecosystems. Operations research is the use of analytical techniques such as mathematical modelling to analyse interactions between people, resources and the environment to aid decision-making in complex systems. Fire managers operate in a highly challenging decision environment characterised by complexity, multiple conflicting objectives and uncertainty. We assert that some of these difficulties can be resolved with the use of operations research methods. We present a range of operations research methods and discuss their applicability to wildfire management with illustrative examples drawn from the wildfire and disaster operations research literature.

A transdisciplinary approach for formalized integrated planning and decision-making in complex systems

Multi-sectoral systems, such as cities or regional economies, face strategic challenges of optimal development due to the complexity of interacting perspectives, interests, and preferences of decision-makers and stakeholders. In coping with these challenges, integrated approaches in strategic planning and decision support promise to generate more efficient and effective results than sectoral approaches. In this article, we follow the concept of operational research (OR) by applying a formalized approach to cross-sectoral integrated planning and decision-making in complex, i.e., multi-sectoral systems. The presented Transdisciplinary Integrated Planning and Synthesis (TIPS) approach relies on (soft) OR methods combined in a multi-methodological framework that is adapted to the cognitive skills and habits of the stakeholders and experts involved in mutual and joint learning processes (transdisciplinarity). This methodological contribution structures, formalizes, and empirically illustrates cross-sectoral integrated strategic planning step by step, and evaluates the TIPS methodology against requirements for integrated approaches derived from literature.

Introducing strategic sustainable development in a business incubator

The qualitative action research explores integration of Strategic Sustainable Development (SSD) at Inova business incubator (Karlstad, Sweden). Researchers and incubator agreed that planning with sustainability in mind at a very early stage of company development may have important impacts on future business success and societal welfare. The objective was to answer how SSD could be integrated into the processes at Inova and how Inova staff's and entrepreneurs' understanding of sustainability demonstrably evolve after exposure to SSD. Apart from general research methods, SSD methods and tools were used with three entrepreneurs and business incubator staff. The Five Level Framework for Planning and Decision-making in complex systems and the Templates for Sustainable Product Development approach were central to the study. Inova staff and entrepreneurs began to develop; an understanding of business' dependence on and relationship to broader societal and ecological systems; and an ability to use backcasting from the basic socio-ecological principles of sustainability in business planning. In turn they experienced an increased capacity to identify sustainability related business risks and opportunities. A new incubation process integrating SSD was created. Although the outcome is specific to Inova, it is general enough for other assistance organizations to gain insight from. In conclusion, SSD should be integrated as early as possible in the business planning of start up companies. Organizations with a mission of assisting entrepreneurs have an important role to play In further research it is recommended that visionary sustainability leaders are identified and tracked from start up.

Extensions of the analytic hierarchy process in fuzzy environment

The aim of this paper is to extend the analytic hierarchy process (AHP) to the fuzzy environment. Firstly, starting from the viewpoint of actual application, considering the method of group decision-making in complex systems and the fuzziness of judgment in pairwise comparison of projects, we construct the fuzzy judgement matrix by using a set-valued statistics method on continuous judgment matrix and prove that every element of the fuzzy judgment scale can be represented by a positive bounded closed fuzzy number. After a discussion of the properties of a positive bounded closed fuzzy number, the fuzzy weight vector of projects can be introduced by applying the fuzzy extension principle to the altered gradient eigenvector method. Thus, the rank-ordering of projects is given. Finally, we also give an example to apply our model.