Decision Logic Research Articles

Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring.

The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed. Fog-Cloud architecture-driven for IoMT networks • An IoMT framework is designed with important components and functions such as fog and cloud node capabilities. •Real-time data has been obtained from WBANs in Riverbed Modeler for a more realistic performance analysis of IoMT. •Fuzzy logic and machine learning algorithms (RF, SVM, and ANN) are used for diabetes predictions. •Intra and Inter-WBAN communications (IEEE 802.15.6 standard) are modeled as essential components of the IoMT framework with all functions.

Does TMT internationalization promote corporate digital transformation? A study based on the cognitive process mechanism

PurposeCorporate digital transformation (DT) and top management team (TMT) internationalization seem to be concomitant phenomena in recent years, the former is a major change and a thorough transformation requiring continuously new technologies and ideas. Excitingly, the latter shows a relatively broad vision, a large risk appetite and interest in new things. Does TMT internationalization promote DT? This question is essential for DT. Given that, this article will aim to discuss and examine whether and how TMT internationalization affects corporate DT.Design/methodology/approachThis article takes China's A-share listed manufacturing companies from 2011 to 2019 as a sample. The quantitative text analysis method is used to measure attention related to digitalization. This paper discusses: (1) The decision-making logic and cognitive process mechanism of “TMT internationalization–attention related to digitalization–corporate DT”. (2) The moderating effect of shared corporate mission of the TMT on the cognitive process of decision-making, that is, the social process of transforming individual cognition into team cognition, and the social process of transforming team cognition into corporate decision-making.FindingsTMT internationalization promotes DT. As an external manifestation of team cognition, attention plays a positive role as an intermediary mechanism. Specifically, executives with overseas experience have higher urgency assessment and manageable assessment, thus affecting their attention to digitalization positively, thereby promoting DT. This article does not demonstrate the moderating effect of shared corporate mission on the cognitive process, but it promotes DT directly, and only plays a role in the precognitive stage.Originality/valueThis article is the first one to study the relationship between TMT internationalization and corporate DT, which has practical guiding significance for DT and the “going out” strategies of the TMT. Also, the combination of upper echelons theory and cognitive theory opens up the black box of the strategic process. Lastly, this research explores the formation process of team cognition, which is always neglected by previous studies of the TMT demographic characteristics.

The Influence of Prior Experience on Entrepreneurs' Decision-making Logic: The Moderating Effect of Environmental Uncertainty

The Influence of Prior Experience on Entrepreneurs' Decision-making Logic: The Moderating Effect of Environmental Uncertainty

SYSTEM OF ENSURING NATIONAL SECURITY AS COMPONENT OF PUBLIC ADMINISTRATION OF THE COUNTRY

The article is devoted to the development of a methodology for building a public administration system that would most adequately correspond to the conditions of functioning of the system ensuring national security and the logic of decision-making in this area. The basis of the methodology for building a system of public management of national security provision is the comprehensive use of elements of the dynamic model of management of the system of national security provision. The main functional subsystems of the national security system and their purpose are shown. The spheres of state activity are directly transformed into spheres (directions) of public administration. One of the important areas of public administration is ensuring national security. The concept of public policy of ensuring national security (PPZNB), its content and principles, as well as the main goals of achieving its effectiveness are presented. It is emphasized that an important feature of public management of ensuring national security is that it is a category of strategic public management that is perspective-oriented, a conceptual plan for improving the management system. The most complex type of strategic management is called «strategic management on a real-time scale» and is usually associated with solving strategic tasks that arise suddenly. In fact, the organization should simultaneously be engaged in clarifying the strategy and solving emerging strategic tasks. It is the system of public management of ensuring national security that is such. This system of strategic public management is at the stage of formation, therefore its components have not yet been definitively determined. Improving the foundations of the public policy of ensuring national security, substantiating its goals, principles and priority areas, mechanisms for achieving the specified goals is impossible without modeling the processes of management decision-making in this area. Modeling also makes it possible to predict the possible negative consequences of management decisions made in various spheres of the state’s life.

End-of-life decision support to enable circular economy in the automotive industry based on digital twin data

With the EU Green Deal and the UN Sustainable Developments Goals, the vision of a greenhouse gas-neutral and resource-efficient economy is already firmly anchored in world politics. In this context, the automotive industry faces many challenges including the increasing scarcity of natural resources, a rising demand in terms of sustainable vehicle design, production and materials sourcing. Due to all this, end-of-life decisions regarding dismantling have become increasingly important. A high proportion of secondary materials will be required in the vehicles of the future. To response to these challenges, companies have turned their focus towards the circular economy as a central approach to close material loops. In the German research project “Catena-X” a new data ecosystem with digital twins is one enabler that is being developed. The digital twins represent a promising approach to the circular economy by ensuring transparent, product-specific and end-to-end data exchange throughout the entire product lifecycle, from the material sourcing to the eventual dismantling and recycling. As one particular and unique solution, a decision framework that facilitates the best circular strategy at the end of a vehicle's life is developed and presented in this paper. The underlying data-driven decision support framework is based on circular economy KPIs. This includes material, components and specific vehicle KPIs to best identify the most suitable circular strategy. The framework was methodologically developed by an interdisciplinary team of partners, who are stakeholders throughout the value chain and participants in the Catena-X project. An integrated approach of user-centered design, an adapted version of the V-model and the Scaled Agile Framework were used for the methodology in the development of the solution. The paper presents the concept of a digital twin for a decision support system, that includes a central decision logic that also includes the relevant KPIs and a survey for evaluating the decision logic utilised with a chosen dismantling company.

Features of decision-making in patients with alcohol dependence

To identify electroencephalographic correlates of decision-making features in patients with alcohol dependence. Forty men with alcohol dependence were examined as the main group. Thirty people were examined as a control group. The Cambridge Gambling Task and the Iowa Gambling Task were used to study decision-making functions. Additionally, a background electroencephalogram was recorded with the calculation of absolute spectral power indicators. In the main group, the relatively higher indicators of decision-making logic (65 [50; 80]%, p<0.05) were associated with larger values of the spectral power of the theta and alpha rhythm of the electroencephalogram in the central and parietal-occipital leads (R=0.426, p=0.019; R=0.418, p=0.022; R=0.394, p=0.028 and R=0.445, p=0.014; R=0.458, p=0.011; R=0.382, p=0.035, respectively). Difficulties in making decisions based on emotional learning in patients with alcohol dependence were associated with larger values of the spectral power of the beta rhythm of the electroencephalogram in the temporal leads. The study showed that the deficit of decision-making functions in patients with alcohol dependence may be due to an imbalance of the excitation/inhibition mechanisms in the brain.

D3GF: A Study on Optimal Defense Performance Evaluation of Drone-Type Moving Target Defense Through Game Theory

Based on the paradigm shift in modern warfare, ground forces conduct pilot operations of wireless unmanned maneuvering systems, such as tactical drones, in the form of manned and unmanned cooperative tactics after deploying the relevant systems in the battlefield. However, security considerations for relevant systems are limited to the scope of using only the existing end-to-end encryption and public key authentication modules, and no defense strategy to actively respond to specialized cyber-electronic warfare threats has been officially established. To drastically reduce both the potential attack surface and security vulnerabilities of drones employed in network-centric-warfare, a proactive defense technology that expires the effectiveness of attacks by avoiding invasive action at the target is expected to be essential. Accordingly, this paper proposes the concept of active moving-target-defense (MTD), an element of cyber deception that minimizes the rate of success of cyber-attacks while conversely maximizing both defense predominance and attack complexity asymmetrically, exclusive according to partially observable Markov decision process (POMDP)-based threat modeling that considers both the internal and external operation sequences of target drones. To optimally design the proposed drone-type MTD based on the Pareto frontier, we additionally advanced and simulated a drone-based defensive deception game framework (D3GF), which represents a general-sum combat framework reflecting decision logics such as the perfect Bayesian Nash equilibrium, stochastic Stackelberg, and partial signal game. This study was conducted to compare and calculate the efficiencies of the drone-type MTD’s deceptive defense, which had not been considered in prior studies, by unique environmental features inside and outside the drone. Furthermore, we conducted a detailed performance evaluation considering game metrics based on sensitivity analysis. Hereafter, the drone-type MTD will be extended into an actual active drone protection technology combining cyber flare-type avoidance strategies and cyber camouflage-type disarrangement strategies by expanding its optimization domain as a hypergame, while integrating it with drone decoy elements.

Poyen’s Fuzzy Logic Controlled Automatic Irrigation (FCAI): Precision Irrigation Scheduling Scheme

Water wastage is a grave problem in irrigation due to outdated methods. This work illustrates how a well-structured fuzzy rule base can eliminate the issue and utilize water at an optimized level. Poyen’s fuzzy logic controlled automatic irrigation (Poyen’s FCAI) efficiently manages the water requirement needs in irrigation processes considering the evapotranspiration (ET) loss and cultivating conditions. Poyen’s FCAI essentially comprises two modules, the wireless sensor network (WSN) and information processing module to perform the assigned task effectively and efficiently. The wireless sensor unit (WSU) accumulates the necessary data, determines the ET loss, and runs the decision logic before conveying it to the execution unit, i.e., the wireless information processing unit (WIPU). The WIPU executes the decision by manipulating the water delivery system, ensuring that the field receives the optimum volume of water. This article illustrates the fuzzy model with simulation output. The model is field-tested with and without the control action over two seasons and in adjacent plots. The results show promising improvements in both water management and crop yield due to more accurate conditions. Hence, we obtain a superior automatic irrigation prototype model for all soil types, across different growth stages, and for all crops with an exhaustive lookup table in place.

Use of Machine Learning to classify clinical research to identify applicable compliance requirements

Background and objectiveDifferent regulatory requirements apply for clinical research activities, thus identifying the applicable regulatory requirements is one of the cornerstones of regulatory compliance. Typically, classification is a very manual and time-consuming process. To make the classification more efficient, precise and dynamic, we proposed to automate the classification process using an advanced Machine Learning algorithm which follows a set of logical decision steps. The main objective of the project was to prove or reject the hypothesis of whether a Machine Learning model can be trained to automatically classify clinical research. MethodsMultiple Machine Learning models based on Natural Language Processing Classifiers including Random Forest, Support Vector Machine, and Logistic Regression were trained to classify Interventional Clinical Trials, Non-interventional Studies and other Real-world evidence research activities based on the text extracted from a Research Plan or a Study Protocol/Synopsis. Starting with Data Exploration and Data Preparation, for Data Preprocessing, the Term Frequency-Inversed Document Frequency (Tf-idf) column transformation was performed. To assess the performance of the models and choose the most efficient one, the accuracy, precision, recall (also known as sensitivity) and f1-score were calculated for each model. ResultsAll models showed sufficient performance. However, the Random Forest Classification model outperformed the other solutions tested and therefore was implemented in our Machine Learning application. The hypothesis was accepted based on a recall score of 1.00 as well as a 0.94 accuracy. The probability score of the result was analysed from the perspective of prioritisation of compliance needs minimising the false negatives for interventional and non-interventional studies. This Machine Learning model was then applied directly to achieve successful Clinical Research classification. ConclusionThus, we have demonstrated that the implementation of the Machine Learning approach helps in the acceleration of the classification process and at the same time increasing compliance with applicable regulatory requirements. This model can benefit the biopharmaceutical industry and individual researchers.

Relationship between the Corporate Disclosure and Profitability: An Empirical Analysis of the Commercial Banks in Bangladesh

Disclosure is essential for interested users to make logical decisions. This study evaluates the extent of corporate disclosure and its relationship with the profitability attributives of banking companies in Bangladesh. To achieve the study purpose, this study has taken five private commercial banks in Bangladesh for five years from 2016 to 2020. The required data have been collected mainly from secondary sources. To reach the study findings per study objectives, the researcher has employed different statistical techniques, analyzed the various annual reports to calculate the level of corporate disclosure, and identified the different profitability attributives. The results of descriptive statistics show that the highest mean value is visible in the case of SB_3, and the lowest mean value is visual in the case of SB_4, but SB_4 and SB_5 show the lowest and highest standard deviation values. The ANOVA test results show significant variation in the level of corporate disclosure and different profitability attributes among the sample banks over the study period. The simple regression results show a substantial relationship between the corporate disclosure scores and profitability attributives. The effects of multiple regression present that other profitability attributes like profit before tax and earnings per share significantly impacted corporate disclosure scores. However, the remaining attributes, like operating profit, profit after tax, return on equity, and return on asset, had an insignificant positive impact on the corporate disclosure scores of the sample banks over the study period. This research study has recommended that the management of the banking companies should take necessary steps to ensure an adequate level of corporate disclosure in reporting practices because the different stakeholders make any decision based on disclosing information.

Empower saving energy into smart communities using social products with a gamification structure for tailored Human–Machine Interfaces within smart homes

Residential buildings can contribute to save energy and to decrement electricity consumption in the world. On the other hand, the Internet of Things has allowed the implementation of smart homes that can profile the users. Nevertheless, end-users are not accepting the smart homes due to behavioral problems and usability problems with the Human–Machine Interface (HMI) or with the household appliances. As a solution, social products promote the interaction between the smart home and the consumer by including gamification features in the interface. Thus, smart homes can interact and compete with other houses to reduce energy consumption. Therefore, this paper proposes a three-step framework that takes advantage of social products to promote interaction between smart homes within a smart community to reduce electrical energy consumption. The first step collects from the literature review, the characteristics of the end-users, the behavioral and usability problems, and the most common gamification elements that teach, engage and motivate the user to reduce energy consumption. The second step proposes the gamification elements required for a tailored HMI in each social product and smart home through a fuzzy logic decision. The third step evaluates the interaction between social products in smart homes and the users to test which smart home is reducing more energy consumption. Finally, a three-level tailored gamified mock-up is depicted: level 1 for a single social product, level 2 for the smart home, and level three for the smart community. This mock-up can be implemented in small communities as residential complexes.

States of Uncertainty, Risk–Benefit Assessment and Early Clinical Research: A Conceptual Investigation

It can be argued that there is an ethical requirement to classify correctly what is known and what is unknown in decision situations, especially in the context of biomedicine when risks and benefits have to be assessed. This is because other methods for assessing potential harms and benefits, decision logics and/or ethical principles may apply depending on the kind or degree of uncertainty. However, it is necessary to identify and describe the various epistemic states of uncertainty relevant to such estimates in the first place. Therefore, this paper aims to develop a category system of different epistemic states of uncertainty which, although not exclusively, is primarily intended to be applied to early clinical trials. It is formed on the basis—and various combinations—of three dimensions of uncertainty that represent certain parts of incomplete knowledge: outcome (type of event), probability (of outcome) and evaluation (assessment of outcome). Furthermore, it is argued that uncertainty can arise from three different sources (the structure of the object of research, the state of the evidence, or individual handling of the research and already existing knowledge). The categories developed are applied to actual examples from gene therapy and genome editing to illustrate that they can be helpful for a more precise definition of the respective uncertainties, especially in the context of risk–benefit assessment. The categories allow a differentiated perspective of decision-making situations from the point of view of incomplete knowledge in general, but particularly, for example, in early clinical research, and may thereby support a more acceptable ethical assessment of potential harms and benefits.

A nascent design theory for explainable intelligent systems

Due to computational advances in the past decades, so-called intelligent systems can learn from increasingly complex data, analyze situations, and support users in their decision-making to address them. However, in practice, the complexity of these intelligent systems renders the user hardly able to comprehend the inherent decision logic of the underlying machine learning model. As a result, the adoption of this technology, especially for high-stake scenarios, is hampered. In this context, explainable artificial intelligence offers numerous starting points for making the inherent logic explainable to people. While research manifests the necessity for incorporating explainable artificial intelligence into intelligent systems, there is still a lack of knowledge about how to socio-technically design these systems to address acceptance barriers among different user groups. In response, we have derived and evaluated a nascent design theory for explainable intelligent systems based on a structured literature review, two qualitative expert studies, a real-world use case application, and quantitative research. Our design theory includes design requirements, design principles, and design features covering the topics of global explainability, local explainability, personalized interface design, as well as psychological/emotional factors.

Design and Validation of Lifetime Extension Low Latency MAC Protocol (LELLMAC) for Wireless Sensor Networks Using a Hybrid Algorithm

As the battery-operated power source of wireless sensor networks, energy consumption is a major concern. The medium-access protocol design solves the energy usage of sensor nodes while transmitting and receiving data, thereby improving the sensor network’s lifetime. The suggested work employs a hybrid algorithm to improve the energy efficiency of sensor networks with nodes that are regularly placed. Every node in this protocol has three operating modes, which are sleep mode, receive mode, and send mode. Every node enters a periodic sleep state in order to conserve energy, and after waking up, it waits for communication. During the sleep mode, the nodes turn off their radios in order to reduce the amount of energy they consume while not in use. As an added feature, this article offers a channel access mechanism in which the sensors can send data based on the Logical Link Decision (LLD) algorithm and receive data based on the adaptive reception method. It is meant to select acceptable intermediary nodes in order to identify the path from the source to the destination and to minimize data transmission delays among the nodes in the network scenario. Aside from that, both simulation and analytical findings are used to examine the activity of the suggested MAC, and the created models are evaluated depending on their performance. With regard to energy consumption, latency, throughput, and power efficiency, the result demonstrates that the suggested MAC protocol outperforms the corresponding set of rules. The extensive simulation and analytical analysis showed that the energy consumption of the proposed LELLMAC protocol is reduced by 22% and 76.9% the end-to-end latency is 84.7% and 87.4% percent shorter, and the throughput is 60.3% and 70.5% higher than the existing techniques when the number of node is 10 and 100 respectively.

Effectuation, causation, and machine learning in co-creating entrepreneurial opportunities

In creating innovative entrepreneurial opportunities, entrepreneurs are characterized by limited information processing capabilities and local search routines that are immanent to humans. Machine learning (ML) offers the opportunity to overcome these limitations while reshaping the innovation process. It is indisputable that ML alone cannot realize entrepreneurial opportunities, but that close collaboration with the human entrepreneur is required. While entrepreneurs usually act according to the principles of effectuation logic in situations of high uncertainty and according to the principles of causation logic in situations of risk, it remains unclear how co-creation with ML affects the entrepreneur's decision-making behavior. By contrasting the functionalities of four different ML paradigms with the principles of the two decision logics, it is shown that supervised ML supports causation logic, while unsupervised and reinforcement ML support effectuation logic in their approach. As a fourth, semi-supervised ML is classified somewhere between effectuation and causation. However, in relation to the situational context of different types of uncertainty, ML may also prove limiting for effectuation by transitioning to causation in the medium-term.

Maximum power extraction and DC-Bus voltage regulation in grid-connected PV/BES system using modified incremental inductance with a novel inverter control

Low ripples and variations in the DC-Bus voltage in single-phase Photovoltaic/Battery Energy Storage (PV/BES) grid-connected systems may cause significant harmonics distortion, instability, and reduction in power factor. The use of short-life electrolytic capacitor on the DC-Bus is considered a standard way for reducing these ripples and variations because of its large capacitance but results in short lifetime of the inverter. Replacing large electrolytic capacitors with small film capacitors can extend the lifetime of a PV/BES grid-connected system because small film capacitors have longer lifetime than large electrolytic capacitors. These film capacitors have low capacitance, which causes severe oscillations in the output current, and voltage drop due to huge ripples on the DC-Bus voltage. In this research, the main goal is to eliminate the output current ripples and voltage fluctuations associated with employing film capacitors. First, a modified incremental conductance (MIC) technique is proposed for tracking the maximum power by controlling the duty ratio of the DC-DC boost converter. Second, for the first time, a simple and novel d-q current regulation technique, which employs flowchart decision logic, is used in the DC-Bus control system for both the PV power system and the state of charge (SOC) of the BES. In this case, the DC-Bus controller is characterized by a cost-effective implementation because of its low sampling frequency. Although the presented approaches are successful in eliminating voltage distortion and fluctuations, they have unacceptable dynamic performance. Therefore, to improve the dynamic performance, BES was used to maintain a reliable and stable harvest from PV modules for varying loads while also increasing the dynamic performance of the overall system. The proposed PV/BES grid-connected systems, which employs a small 10-µF bus capacitor, is simulated and connected to the grid (230 V, 50 Hz). The DC-Bus voltage overshoot, undershoot and the total harmonics distortion (THD) of the output current for the proposed MIC are (1 V), (2.5 V) and (less than 5%), respectively. The average time response under rising radiation to track the global peak for MIC, traditional incremental conductance and variable step size incremental conductance are 1.403 s, 1.501 s and 1.113 s respectively. The obtained findings demonstrated the efficacy and superiority of the proposed d-q current control and MIC technique.

Effectuation, SME service innovation, and business customers’ value perception

ABSTRACT Despite extensive research focus on the role of resource integration and combination in service innovation, the influence of decision-making logic has not been scrutinized. This study aims to examine the influence of effectuation, a distinct control-oriented decision-making logic, on small and medium-sized enterprises’ (SMEs) service innovation. Based on a survey of Chinese manufacturing SMEs, the findings show that effectuation has an inverted U-shaped relationship with incremental service innovation but a linear positive relationship with radical service innovation. Moreover, environmental uncertainty reinforces the relationship of effectuation with incremental and radical service innovation. Finally, the relationship with business customers’ value perception is positive for incremental service innovation but inverted U-shaped for radical service innovation. The findings offer unique insights into the importance of effectuation for SMEs’ service innovation in uncertain environments, as well as for customers’ value perception in the business market.

Irrational Behaviour in Financial Decision-Making

The goal of the research questions is analysing empirically if there is any irrational behaviour in decision-making and if human behaviour is not always rational using not consistent logic in problem solving or decision-making. The researcher will analysis the behaviour of the investor during bearish and bullish market to find behaviour inconsistency, that according to the researcher, could be due to the asymmetric anchor effect, asymmetric herd behaviour, asymmetric disposition effect and asymmetric, loss aversion, recency bias, frame bias. Keywords: herd behaviour, decision making, disposition effect, asymmetric behaviour, anchor bias, disposition effect, level-K strategy, recency bias, loss aversion, prospect theory, disposition effect, recency bias, frame bias,irrational behaviour or inconsistent logic in decision making and problem solving.

An Integrated Model for Autonomous Speed and Lane Change Decision-Making Based on Deep Reinforcement Learning

The implementation of autonomous driving is inseparable from developing intelligent driving decision-making models, which are facing high scene complexity, poor decision-making coupling, and the inability to guarantee decision-making safety. This paper starts with the priority and logic of lane change and car-following decision-making, considering driving efficiency, safety, and comfort, then constructs a double-layer decision-making model. This paper uses two deep reinforcement learning algorithms for the upper and lower layers to process large-scale mixed state space and ensure the composite action output of lane-changing decisions and car-following decisions. In the upper layer model, we use the D3QN algorithm to distinguish the potential value of the environment and the value of selecting lane-changing actions when making lane-changing decisions. Different from the traditional mechanisms that only use negative rewards, the lane changing benefit function and dangerous action shielding mechanism are used to eliminate collisions. DDPG algorithm is adopted in the lower layer model to process car-following decisions and output continuous vehicle speed control. Besides, coupled training is taken for the two algorithms to improve the coordination of the double-layer model. This paper selected mixed standard driving cycle conditions to build a highly complex training environment and used NGSIM data to reconstruct scenes to test our model. Simulations in SUMO are presented that the double-layer model can increase the driving speed of the original data by 23.99%, which has higher effectiveness than other models.

Real-Time Grid and DER Co-Simulation Platform for Testing Large-Scale DER Coordination Schemes

Distributed energy resources (DERs), such as responsive loads and energy storage systems, can help grid operators to more effectively balance supply and demand and manage network constraints. However, consumer acceptance of load coordination schemes depends on ensuring customer quality of service (QoS), thus requiring the careful management of device-level constraints. Simultaneously managing both device-level and system-level objectives is a difficult problem. Most potential solutions to the DER coordination problem are in some way distributed, with decision logic running both at the device (such as a thermostat) and the coordinator, with potentially complex communications between devices and one or more coordinators. It is difficult to effectively test distributed DER control schemes, particularly when there are thousands of devices with both device-level and grid-level constraints and asynchronous communications. This paper addresses this challenge by presenting a novel real-time grid-and-DER cyber-physical co-simulation platform for testing advanced DER coordination schemes and characterizing the behaviors of a DER fleet. The co-simulation platform is particularly suitable for: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> ) testing the real-time performance of a large fleet of DERs delivering advanced grid services; <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii$ </tex-math></inline-formula> ) characterizing the distribution of states for a large fleet of managed DERs; and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$iii$ </tex-math></inline-formula> ) incorporating practical limitations of DERs and communications and analyzing the effects on fleet-wide performance. To illustrate these benefits, we demonstrate how the platform can be used to test a fleet with thousands of DERs coordinated using Packetized Energy Management (PEM) and interacting with a realistic transmission system model.