Collaborative Decision Model Research Articles

Collaborative task decision-making of multi-UUV in dynamic environments based on deep reinforcement learning

ABSTRACT The problem of collaborative task decision-making for unmanned underwater vehicles (UUVs) in an unknown dynamic ocean environment is investigated. In view of the unknown marine environment, a partially observable Markov decision process is designed to achieve partially observable path planning for multi-UUV. Aiming at the problems of long planning time, large amount of data, and insufficient multi-task decision-making capability in the process of multi-UUV underwater collaborative operation, a multi-UUV collaborative task decision-making model based on the multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm for the dynamic environment is constructed. The implementation of the centralised training with distributed execution (CT-DE) training framework enables each UUV to possess autonomous decision-making capability and ensures task safety under weak communication conditions. The pre-training phase shows that the MATD3 algorithm outperforms the multi-agent deep deterministic policy gradient (MADDPG) algorithm for training multi-UUV autonomous decision-making scenarios in dynamic environments. The simulation experiment results verify that the autonomous decision-making method based on the MATD3 algorithm can effectively solve the multi-UUV collaborative task decision-making problem in unknown dynamic environments, and the task process satisfies the requirements of real-time, safety and economy.

Modelling of Flood Hazard Early Warning Group Decision Support System

Early warning of flood hazards needs to be carried out comprehensively to avoid a higher risk of disaster. Every decision on early warning of a flood hazard is carried out in part by one party, namely the government or water resource managers. This research aims to provide a collaborative decision-making model for early warning of flood hazards through a Group Decision Support System Model (GDSS), especially in Indonesia. The novelty of this research is that the GDSS model involves more than one decision-maker and multi-criteria decision-making for early warning of flood hazards in the downstream Kali Sadar River, Mojokerto Regency, East Java Province, Indonesia. The GDSS model was developed using a hybrid method, namely the Analytical Network Process (ANP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). There was more than one decision result; voting was carried out using the BORDA method to produce the decision. The test results of GDSS were obtained using a Spearman rank correlation coefficient of 0.8425 and matrix confusion, an accuracy value of 86.7%, a precision value of 86.7%, a recall value of 86.7%, and an f-measure of 86.7%. Based on the test results, good results were obtained from the GDSS model. Doi: 10.28991/CEJ-2024-010-02-018 Full Text: PDF

Analysis of factors that promote the participation of patients with chronic diseases in shared decision making on medication: a cross-sectional survey in Hubei Province, China

BackgroundShared decision making (SDM) improves the health status of patients with chronic diseases, especially in the condition of poly-medicated patients. This study aims to find the factors associated with participation of patients with chronic diseases in SDM on medication.MethodsA total of 1,196 patients with chronic diseases were selected in Hubei Province of China using cluster sampling methods. The random forest method was applied to rank the importance of independent variables by Mean Decrease Gini and out-of- bag (OOB) curve. Multivariate logistic regression was used to explore the independent variables’ effect direction and relative hazard.ResultsIn this study, 5.18% of patients used patient-directed decision making (PDM, a decision-making model led by patients), 37.79% of patients used SDM (a collaborative decision-making model by patients and doctors), and 57.02% of patients used doctor-directed decision making (DDM, or paternalistic decision making, a decision-making model led by doctors). The random forest analysis demonstrated that the top 5 important factors were age, education, exercise, disease course, and medication knowledge. The OOB curve showed that the error rate reached minimum when top 5 variables in importance ranking composed an optimal variable combination. In multivariate logistic regression, we chose SDM as a reference group, and identified medication knowledge (OR = 2.737, 95%CI = 1.524 ~ 4.916) as the influencing factor between PDM and SDM. Meanwhile, the influencing factors between DDM and SDM were age (OR = 0.636, 95%CI = 0.439 ~ 0.921), education (OR = 1.536, 95%CI = 1.122 ~ 2.103), exercise (OR = 1.443, 95%CI = 1.109 ~ 1.877), disease course (OR = 0.750, 95%CI = 0.584 ~ 0.964), and medication knowledge (OR = 1.446, 95%CI = 1.120 ~ 1.867).ConclusionMost Chinese patients with chronic diseases used DDM during their medication decision-making, and some patients used PDM and SDM. The participation in SDM should be taken seriously among elderly patients with lower education levels. The SDM promotion should focus on transformation of patients’ traditional perception and enhance their medication knowledge.

Collaborative emergency decision-making: A framework for deep learning with social media data

Emergency decision-making (EDM) problems based on social media data have recently attracted considerable attention. However, few studies have considered collaborative EDM based on public opinion and expert knowledge. To improve the effectiveness and interpretability of EDM, we propose a knowledge+opinion driven multi-phase collaborative emergency decision-making model, which combines social media data that represents public opinion with the knowledge and experience of experts. First, a text-mining algorithm extracts the keywords and their weights from the social media data. Then, we define 2-tuple emergency attributes to simplify and quantify the keywords with social media data. Furthermore, a sentiment analysis model based on the XLNet-Att deep learning algorithm is proposed to obtain sentiment polarities for emergencies and provide timely support for government EDM in the future. Moreover, a real-world case concerning the Southern China flood disaster in 2020 is applied to validate our proposed model. We find that for similar emergencies, the focus of public attention have similar characteristics at different periods, and the analysis results show different perspectives of public attention to emergencies at different stages, providing reliable data and experience support for future EDM of similar emergencies. Finally, we conduct a sensitivity analysis to demonstrate the stability of our deep learning model and a comparative study using existing models to verify the effectiveness of our model.

An Optimal Operation Strategy for Collaborative Flexibility Provision of a Carbon Capture and Utilization Process With Wind Energy

Improving power system flexibility by responsive demand is essential for integrating wind energy with a high level of variability in power systems. Carbon dioxide-based chemical processes as energy-intensive industrial loads may offer a vast potential of new forms of flexible operation due to their existing control infrastructure and storage capabilities. However, a collaborative decision model is needed for optimal energy sharing among the chemical plant and the grid under the variations and uncertainties of wind power. This study develops an optimal two-stage stochastic programming model for a novel flexible operation strategy of the chemical process coupled with wind turbines. In the proposed control scheme, a small-scale wind farm provides the power input of a chemical plant. Wind turbines are connected to the grid and actively participate in the day-ahead energy and reserve markets, considering the chemical plant as a source of flexibility. An equivalent scenario-based model of the proposed optimization problem is suggested using the Group Method of Data Handling (GMDH) for a data-driven prediction of stochastic variables. Simulation results demonstrate the effectiveness and significance of the proposed approach for an optimal and collaborative contribution in ancillary market of a carbon dioxide-based chemical plant supplied by wind energy.

Optimizing collaborative decision-making of multi-agent resources for large-scale projects: from a matching perspective

PurposeIn recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.Design/methodology/approachThe main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.FindingsNumerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.Research limitations/implicationsWhile the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.Practical implicationsCompared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.Social implicationsLarge-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.Originality/valueThe current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

Collaborative decision-making model for capacity allocation of photovoltaics energy storage system under Energy Internet in China

Solving the problem of photovoltaics abandonment and power limitation and improving resource utilization is particularly important to promote the sustainable development of the PV industry. With the innovative development and continuous application of energy storage technology, energy storage has become an indispensable part of photovoltaic power generation, realizing the consuming goal of abandoned photovoltaics. This paper studies the synergistic management of PV power generation based on the perspective of value chain, and constructs a complex value chain system with PV power generation subsystem and energy storage subsystem as the key subsystem—photovoltaics energy storage system (PVESS). Firstly, a value co-creation analysis framework for promoting capacity allocation of PVESS under the Energy Internet is analyzed. Secondly, the basic model of hybrid energy storage system (HESS) combining battery energy storage system (BESS) and superconducting magnetic energy storage system (SMES) is constructed. Thirdly, a multi-objective collaborative decision model is established with the objective functions of minimum economic cost, minimum abandoned photovoltaics and minimum load outage rate for the hybrid energy storage system (HESS). And the hybrid particle swarm algorithm (HPSO) algorithm and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method are used to determine the optimal system configuration. Finally, the validity and feasibility of the model are proved by the results of collaborative decision making and sensitivity analysis. The proposed collaborative decision suggestions for capacity allocation is adopted to facilitate the collaborative and effective operation of the system. This study can also provide insightful enlightenment for PVESS project investors, collaborative decision-making professionals and decision makers.

Supply-hub-based Collaborative Replenishment Decisions for Enterprise Supply Logistics Under Demand Uncertainty

To address the collaborative replenishment problem of enterprise supply logistics, a Supply-hub-based collaborative replenishment decision model is constructed from the perspective of horizontal collaboration among suppliers and the introduction of component demurrage costs and product out-of-stock penalty costs. The number of suppliers participating in the system, the replenishment level of suppliers and the impact of demand uncertainty on the operational cost of supply logistics are analysed. The results show that: the participation of multiple suppliers can reduce the increase of supply logistics operating costs to a certain extent; as the service level increases, the supply logistics operating costs increase and then decrease; the impact of demand uncertainty on the supply logistics operating costs can be mitigated to a certain extent by the coordination function of Supply-hub.

Collaborative decision-making of multi-source heterogeneous sensing data for smart power IoT

The current multi-source heterogeneous sensing data collaborative decision-making method based on machine learning achieves the fusion of sensing data through data sensing technology, which leads to the poor comprehensive collaborative performance of the model due to the low data processing accuracy. In this regard, multi-source heterogeneous sensing data collaborative decision-making for smart power IoT is proposed. A data reliability analysis framework is established to analyze the unstable factors affecting the fluctuation of sensing data, and the data is slimmed down. And the feature vectors of the sensed data are extracted to establish the data collaborative decision-making model. In the experiments, the comprehensive collaborative performance of the proposed method is verified. The analysis of the experimental results shows that the collaborative decision model of sensing data constructed by the proposed method has a high level of comprehensive evaluation and its collaborative decision performance is high.

Collaborative Decision Model for Allocating Intensive Care Units Beds with Scarce Resources in Health Systems: A Portfolio Based Approach under Expected Utility Theory and Bayesian Decision Analysis

The COVID-19 pandemic has brought health systems to the brink of collapse in several regions around the world, as the demand for health care has outstripped the capacity of their services, especially regarding intensive care. In this context, health system managers have faced a difficult question: who should be admitted to an intensive care unit (ICU), and who should not? This paper addresses this decision problem using Expected Utility Theory and Bayesian decision analysis. In order to estimate the chances of survival for patients, a structured protocol has been proposed conjointly with physicians, based on the Sequential Organ Failure Assessment (SOFA) score. A portfolio selection approach is proposed to support tackling the ICU allocation problem. A simulation study shows that the proposed approach is more advantageous than other approaches already presented in the literature, with respect to the number of lives saved. The patients’ probabilities of survival inside and outside the ICU are important parameters of the model. However, assessing such probabilities can be a difficult task for health professionals. In order to give due treatment to the imprecise information regarding these probabilities, a Monte Carlo simulation is used to estimate the probabilities of recommending a patient be admitted to the ICU is the most appropriate decision, given the conditions presented. The methodology was implemented in an Information and Decision System called SIDTriagem, which is available online for free. With regards to managerial implications, SIDTriagem has a great potential to help in the response to public health emergencies systems as it facilitates rational decision-making regarding allocating ICU beds when resources are scarce.

Socio-spatial dimensions of school closures and neighbourhood change in Ontario: An environmental injustice?

In addition to their educational purposes, public schools and their surrounding properties are essential to community liveability, as they enrich the daily lives of children, parents, and nearby residents. Yet, decisions are being made to close schools in Ontario, Canada based on declining enrolments, without due consideration of these benefits. Since 2011, over 400 public schools have been closed in Ontario, causing communities across the province to lose essential hubs. In a province where significant socio-spatial inequities persist, public school closures could worsen the conditions of daily living for residents in neighbourhoods that have already been deprived of resources and opportunities through failed public policy. The objectives of this study were to document the spatial scope of public school closures in Ontario, to understand the population change profiles in communities where closures happened, and to elucidate how these closures temporally relate to structural vulnerabilities of the communities in which these closures took place. Using Census-derived deprivation index scores geo-coded dataset to both currently open and recently closed public schools in Ontario, our analysis revealed three key findings. First, school closures have occurred disproportionately in small to mid-sized cities and rural communities. Second, there is no evidence of significantly declining child populations prior to school closures, in communities where schools closed. And third, closures were more common in higher deprivation communities in small to mid-sized cities. Taken together, these findings offer critical insights on the challenges that many communities face due to insufficient and inequitable policies that govern school closure decisions in Ontario. The study signals an urgent need for a more collaborative, forward-thinking, and equity-oriented school closure decision-making model that supports residents and protects communities from losing a vital public asset.

Research on Pattern Matching of Dynamic Sustainable Procurement Decision-Making for Agricultural Machinery Equipment Parts

With the digital transformation of the manufacturing industry and the diversification of production methods of agricultural machinery and equipment, external purchase, external coordination, and self-made products continue to increase. If agricultural machinery manufacturing enterprises want to maintain maximum benefits in the fierce competition, they must pay attention to the collaborative procurement decision-making model and the relationship between production, supply, and marketing, and seek a comprehensive dynamic sustainable procurement strategy under the supply chain environment. In this paper, from the manufacturers of agricultural machinery manufacturing enterprises, firstly, three procurement strategies based on line-side inventory supply, third-party logistics supply, and dynamic sustainable supply are studied respectively, while a system dynamics model of collaborative procurement strategy for the agricultural machinery supply chain is constructed and the three procurement strategy models are simulated and analyzed Secondly, the simulation results are analyzed to establish the measurement indexes for evaluating sustainable procurement model matching, and a procurement model matching measurement model based on the topological superiority of object elements combined with the topological hierarchical analysis method and CRITIC comprehensive assignment method is proposed to determine the index weights. And using the correlation function calculation, we get the comprehensive superiority ranking of procurement patterns and the correlation comparison of individual indicators and output the optimal procurement matching pattern and pattern recognition degree. Finally, an application example is given to verify the correctness and practicability of the proposed decision-making model, to provide a qualitative and quantitative dynamic sustainable procurement multi-attribute decision-making tool for the procurement management of agricultural machinery equipment manufacturing enterprises.

Multi-Granularity Collaborative Decision With Cognitive Networking in Intelligent Transportation Systems

Cognitive networking is a valuable enabler to improve the capability of intelligent transportation system (ITS) by analyzing and utilizing the heterogeneous traffic information. However, the significant increase in the amount of decision-making tasks makes it difficult to guarantee real-time performance of decision response. This paper focuses on the problem of the quality and real-time assurance of collaborative decision-making response in large-scale ITS during multi-task parallelism execution. First, a collaborative decision architecture with cognitive networking is developed, which introduces the advanced 6G communication technology to enhance information interaction capability of vehicle-road-cloud collaboration, and lays the foundation for multi-task real-time decision-making with inevitable fuzzy information in the perception process. Then, a multi-task parallel multi-granularity collaborative decision model (MPMCD) is designed to improve knowledge discovery ability for decision-making process by building multi-granularity information structures. An AI-driven cognitive networking collaborative decision-making (ACNCD) algorithm is further proposed based on MPMCD model to support multi-task parallel vehicle-road-cloud collaborative real-time decision. Extensive simulation experiments are carried out to evaluate ACNCD algorithm in terms of several performance criteria including decision response time, accuracy, and accident rate. The obtained results show that the comprehensive decision-making performance of ACNCD outperforms other relevant existing algorithms.

Collaborative decision-making: A framework for decision-making about life-sustaining treatments in children with medical complexity.

Caregivers of children with medical complexity (CMC) face decisions about life-sustaining treatments (LST) like tracheostomy. We sought to develop a clinically relevant and realistic model for decision-making about tracheostomy placement that might apply to other LST in CMC. This qualitative study, conducted between 2013 and 2015, consisted of 41 interviews with 56 caregivers of CMC who had received tracheostomies and 5 focus groups of 33 healthcare providers (HCPs) at a tertiary-care children's hospital in North Carolina. Participants were asked about their perspectives on the tracheostomy decision-making process. Data were transcribed, and coded. Using thematic content analysis, we inductively developed a tracheostomy decision-making framework and process. Many factors influenced caregivers' decisions, including children's well-being and caregivers' values, faith, knowledge, experience, emotional state, and social factors; preserving the child's life was the most important. HCPs consider many clinical and nonclinical factors; recommending tracheostomy for children with limited survival, perceived poor functioning and quality of life, and progressive conditions is ethically difficult. The framework of tracheostomy decision-making has inter-related caregiver- and HCP-level factors that influence the process. The framework contains elements not captured in a shared decision-making model, but better fits a collaborative decision-making (CDM) model. The tracheostomy CDM process that emerged from the data has two nonsequential components that HCPs could use: (1) gaining understanding and (2) holding decision-making conversations. CDM could be a useful model for clinicians guiding families about tracheostomy for CMC. The applicability of CDM for decision-making about other LSTs needs further exploration.

Intelligent manufacturing system with human-cyber-physical fusion and collaboration for process fine control

There are some problems in non-ferrous metal industry, such as low production efficiency, separation of manufacturing information, disconnection between production, supply and marketing and so on, which highlight the need for fine process control and business collaborative decision-making with cost, efficiency, quality and safety as the core. On the basis of Human Cyber Physical fusion intelligence, this paper firstly discusses the mechanism of intelligent manufacturing system with HCP fusion and collaboration, and explores the HCP data fusion mechanism, collaboration mechanism and interaction mechanism. Then, an intelligent manufacturing system with HCP fusion and collaboration in the non-ferrous metal industry is established, in which a knowledge-graph based HCP data fusion framework and a cyber-physical system based HCP business collaborative decision model are discussed to realize HCP fusion and collaboration manufacturing. Finally, a case study for a copper smelting enterprise is explored, in which the intelligent manufacturing system architecture for the copper smelting enterprise is proposed, and validated through the collaborative decision-making of production, supply and marketing integration plan in the world’s largest monomer copper smelter enterprise.

Incentive Mechanism Design in Collaborative Management of Public Health Emergencies

Against the background of the worldwide spread of the COVID-19 pandemic, due to the lack of effective collaborative incentive mechanisms, issues of dislocation and incoordination have emerged in the public health emergency management system (PES) globally. Nevertheless, the available research rarely touches upon the incentive mechanism design for the collaborative management of PES. To alleviate these inefficiency operations problems with PES, three game-theoretical decision models, including a decentralized decision model without public governance policy incentive (GPI), a decentralized decision model with GPI, and a collaborative decision model with GPI, were developed and analyzed to explore and design the incentive mechanism of PES. Furthermore, the corresponding numerical and sensitivity analyses were conducted to validate the modelling results in the article. The research results show that: (1) the collaborative decision scenario with GPI performs best, and the decentralized decision scenario with GPI performs second-best regarding the equilibrium emergency management efforts (EMEs) and utilities in all the decision scenarios; (2) an incentive mechanism of the “carrot + Stick” can effectively enhance the collaborative management of public health emergencies and its governance-driven operational performance/efficiency; (3) strengthening the performance assessment of emergency management for relevant government departments is beneficial in improving the overall emergency management efficiency of the PES; (4) reducing the cost of EMEs and enhancing the effectiveness of EMEs are conducive to improving the overall emergency management efficiency of the PES. This research provides a novel framework for designing an effective incentive mechanism to overcome the incoordination impacts and achieve collaborative operations across the PES.

A Systematic Approach for the Interview of the Application to Psychiatry Specialty Training: The “I AM” Approach

AimsJob interviews are the platform for employers to identify suitable candidates for vacant posts, i.e. those who are able to demonstrate a certain set of competencies specified in the job description. In the recent psychiatry specialty training (ST) application process, candidates are required to propose a management plan for two complex scenarios. This interview can be stressful given the high-stakes nature of the outcome, i.e. the successful enrolment into a training programme of a preferred deanery. Candidates who are unable to have an organised approach to problem-solving will likely have an unfavourable result. To overcome this difficulty, a simplified “I AM” approach is being proposed to assist applicants to organise their thoughts during their ST application interview.MethodsThe “I AM” approach stands for “Issues, Assessments, and Management”, which is adapted from the “Handbook of Psychiatry: Surviving Consultation Viva Examination of Malaysian Conjoint Board”. The “Issues” are the problems identified in a scenario, “Assessments” are the investigation required to get a clearer picture of the problems, and “Management” is the action plan to solve the problems. This approach was piloted with five applicants of ST in psychiatry prior to their interview practices.ResultsFor a complex clinical case scenario, the “I AM” approach can be put into the matrix of 3 × 3 tables together with a biopsychosocial model to ensure the issues in different domains are explored thoroughly. Further sub-classification into necessary subheadings, including ideas, concerns, and expectations from different parties, can be included in the assessment matrix. Lastly, a management plan using a multidisciplinary team and collaborative decision-making model with the patient and family can be proposed. For a complex managerial scenario, the seven pillars of the National Health Service's clinical governance model involving different stakeholders can be incorporated into the “I AM” approach to explore problem-solving strategies from different angles. Positive reactions had been received from all five trainees (Kirkpatrick's Evaluation Model Level One).ConclusionThe “I AM” approach can be flexibly applied in different problem-solving scenarios and it works well with other models. The approach may be limited by inadequate information and a failure to prioritise. Further systematic evaluation of the effectiveness and generalisability of the “I AM” approach to other disciplines is required.

Knowing When to Pass: The Effect of AI Reliability in Risky Decision Contexts.

This study manipulates the presence and reliability of AI recommendations for risky decisions to measure the effect on task performance, behavioral consequences of trust, and deviation from a probability matching collaborative decision-making model. Although AI decision support improves performance, people tend to underutilize AI recommendations, particularly when outcomes are uncertain. As AI reliability increases, task performance improves, largely due to higher rates of compliance (following action recommendations) and reliance (following no-action recommendations). In a between-subject design, participants were assigned to a high reliability AI, low reliability AI, or a control condition. Participants decided whether to bet that their team would win in a series of basketball games tying compensation to performance. We evaluated task performance (in accuracy and signal detection terms) and the behavioral consequences of trust (via compliance and reliance). AI recommendations improved task performance, had limited impact on risk-taking behavior, and were under-valued by participants. Accuracy, sensitivity (d'), and reliance increased in the high reliability AI condition, but there was no effect on response bias (c) or compliance. Participant behavior was only consistent with a probability matching model for compliance in the low reliability condition. In a pay-off structure that incentivized risk-taking, the primary value of the AI recommendations was in determining when to perform no action (i.e., pass on bets). In risky contexts, designers need to consider whether action or no-action recommendations will be more influential to design appropriate interventions.

Changing the regulations for regulating the changes: From distribution system operator (DSO) to electricity distribution stakeholders’ organization (EDSO)

Followed by the extensive activation of demand side entities, the anti-competitive and monopolistic aspects of distribution system operator (DSO) is becoming more apparent. The business model of the DSO and its regulations are not capable enough to fully leverage the capacity of emerging technologies and responsive prosumers. This paper criticized the challenges/deficits revolving around the conventional DSO model and proposes an interactive platform based on the sharing economy business model at the network level. The electricity distribution stakeholders’ organization (EDSO) as the substitute of DSO in active distribution networks is developed based on the collaborative governance decision-making model. The EDSO has the privilege to legislate on the platform operation and its main policy is to preserve the public interest as well as the integrity of electric distribution system. The superiority of proposed operation model is its non-monopolistic and non-profit nature which materializes the perfect competition of active customers and guaranties their maximum utilization.

Research on the Decision Mechanism of University-Enterprise Collaborative Innovation Based on Quantum Cognition

The relationship between quantum cognition and decision-making is an important field in the direction of human behavior research. The uncertain and entangled state of quantum cognition has largely explained the decision-making process with limited information and ambiguity. University-enterprise collaborative innovation, a systematic project of joint action by both parties, aims to integrate the resources of enterprises and universities and enhance their innovation capabilities. As the main decision-making parties in collaborative innovation decision-making, the university and enterprise have a state of uncertainty and entanglement in their cognitive preferences before the final decision is formed. A university-enterprise collaborative innovation decision-making model should be constructed from the perspective of enterprise decision-making, taking different innovation schemes as the final choice, and the method of quantum probability theory can be used to perform quantum cognition on the independent and joint decision-making models in the university-enterprise collaborative innovation decision-making. Research shows that the internal mechanism of university-enterprise collaborative decision-making is based on its quantum attributes, university-enterprise collaborative decision-making is more inclined to joint decision-making, joint university-enterprise collaborative decision-making is more inclined to quantum cognition incompatibility expression, and the opinions of innovative members will have sequential effects and interference effects on decision makers.