Agent-Based Modelling for Scenario Analysis in Management Consulting

An Agent-Based Approach to Aircraft Conflict Resolution with Constraints

The green security market has seen critical development because of an uplifted consciousness of natural maintainability and the squeezing need to battle environmental change. This chapter investigates the complexities of green security issuance, the difficulties presented by unfriendly choices, the essential drivers of market extension, and the developing techniques utilized by financial backers. Backers utilize green securities to finance ecologically valuable undertakings while confronting examinations concerning the validity of their green cases. Unfavorable choice remains a huge issue, with the potential for 'greenwashing' compromising business sector respectability. The review distinguishes administrative help, financial backer interest for practical speculations, and monetary advantages of green bonds as key development drivers. Financial backer techniques uncover a pattern toward the thorough expected level of efforts and an inclination for straightforwardness and confirmed green qualifications. The discoveries highlight the significance of hearty norms and administrative structures to keep up with market believability and advance certified ecological effects through green securities. To deepen the analysis, computational modeling approaches can be integrated, offering insights into market dynamics, investor behavior, and the effectiveness of regulatory measures. Techniques such as agent-based modeling, network analysis, machine learning, and scenario analysis can simulate complex market interactions, predict trends, and assess risks, ultimately contributing to the sustainable growth of the green security market. In this chapter, scenario analysis is used to determine the green security market.

Guide for Forecasting Financial Statements and Financial Valuation of a Business Plan (In Spanish)

SEARCH (Scenario evaluation and analysis through repeated cross impact handling): a new method for scenario analysis with an application to the Videotel service in Italy

Modern embedded systems are typically integrated as multiprocessor system on chips, and are often characterized by the complex behaviors and dependencies that system components exhibit. Different events that trigger such systems normally cause different execution demands, depending on their event type as well as on the task they are processed by, leading to complex workload correlations. For example in data processing systems, the size of an events payload data will typically determine its execution demand on most or all system components, leading to highly correlated workloads. Performance analysis of such complex system is often very difficult, and conventional analysis methods have no means to capture the possible existence of workload correlations. This leads to overly pessimistic analysis results, and thus to too expensive system designs with considerable performance reserves. We propose an abstract model to characterize and capture workload correlations present in a system architecture, and we show how the captured additional system information can be incorporated into an existing framework for modular performance analysis of embedded systems. We also present a method to analytically obtain the proposed abstract workload correlation model from a typical system specification. The applicability of our approach and its advantages over conventional performance analysis methods is shown in a detailed case study of a multiprocessor system on chip, where the analysis results obtained with our approach are considerably improved compared to the results obtained with conventional analysis methods.

This study investigates the properties and method of fuzzy scenario analysis. In order to cope with the issues of data shortage and linguistic expression of an expert in a strategic planning, this study incorporates the concept of fuzzy set theory into scenario analysis, so that the uncertainties involved in a strategic planning can be considered. Based on Brauers and Weber's method (1988) in scenario analysis, we proposed a method of the fuzzy scenario analysis. It includes: 1) the analysis of factors, 2) the possible analysis of outcomes, 3) the compatible analysis of scenarios, 4) the possible analysis of scenarios, and 5) the determination of the main scenarios. Following this structure, we can describe the main pictures of future developments with their degrees of possibilities. The propose method has been compared with Brauers and Weber's method and applied to broadband telecommunication strategic planning with satisfactory results.

A method of scenario analysis is proposed to provide the choice of the optimal management system for cost-effective water use and ecological rehabilitation of rivers. Scenario analysis is carried out on the basis of simulation and optimization modeling. The operational management options are simulated and optimized according to Pareto using environmental and economic criteria. The scenario analysis method was used to select the optimal control system for flushing the Ingulets River. The effectiveness of the optimal option is confirmed by comparing the results of scenario analysis with the existing flushing method in 2019 year for indicators of water consumption and environmental criteria for water quality.

Water supply and demand management (WSDM) is essential for developing sustainable cities and societies. WSDM is only effective when tackled from the perspective of a holistic system understanding that considers social, environmental, hydrological, and economic (SEHEc) sub-systems. System dynamics modeling (SDM) is recommended by water resource researchers as it models the biophysical and socio-economic systems simultaneously. This study presents a comprehensive literature review of SDM applications in sustainable WSDM. The reviewed articles were methodologically analyzed considering SEHEc sub-systems and the type of modeling approach used. This study revealed that problem conceptualization using the causal loop diagram (CLD) was performed in only 58% of the studies. Moreover, 70% of the reviewed articles used the stock flow diagram (SFD) to perform a quantitative system analysis. Furthermore, stakeholder engagement plays a significant role in understanding the core issues and divergent views and needs of users, but it was incorporated by only 36% of the studies. Although climate change significantly affects water management strategies, only 51% of the reviewed articles considered it. Although the scenario analysis is supported by simulation models, they further require the optimization models to yield optimal key parameter values. One noticeable finding is that only 12% of the articles used quantitative models to complement SDM for the decision-making process. The models included agent-based modeling (ABM), Bayesian networking (BN), analytical hierarchy approach (AHP), and simulation optimization multi-objective optimization (MOO). The solution approaches included the genetic algorithm (GA), particle swarm optimization (PSO), and the non-dominated sorting genetic algorithm (NSGA-II). The key findings for the sustainable development of water resources included the per capita water reduction, water conservation through public awareness campaigns, the use of treated wastewater, the adoption of efficient irrigation practices including drip irrigation, the cultivation of low-water-consuming crops in water-stressed regions, and regulations to control the overexploitation of groundwater. In conclusion, it is established that SDM is an effective tool for devising strategies that enable sustainable water supply and demand management.

Autonomous ferries are providing new opportunities for urban transport mobility. With this change comes a new risk picture, which is characterised to a large extent by the safe transition from autonomous mode to manual model in critical situations. The paper presents a case study of applying an adapted risk assessment method based on the Scenario Analysis in the Crisis Intervention and Operability study (CRIOP) framework. The paper focuses on the applicability of the Scenario Analysis to address the human-automation interaction. This is done by presenting a case study applying the method on a prototype of a Human–Machine Interface (HMI) in the land-based control centre for an autonomous ferry. Hence, the paper presents findings on two levels: a method study and a case study. A concept of operation (CONOPS) and a preliminary hazard analysis lay the foundation for the scenario development, the analysis, and the discussion in a case study workshop. The case study involved a Scenario Analysis of a handover situation where the autonomous system asked for assistance from the operator in a land-based control centre. The results include a list of identified safety issues such as missing procedures, an alarm philosophy and an emergency preparedness plan, and a need for explainable AI. Findings from the study show that the Scenario Analysis method can be a valuable tool to address the human element in risk assessment by focusing on the operators’ ability to handle critical situations.

Including learning and forgetting processes in Agent-Based simulation models: Application to police intervention in out-of-hospital cardiac arrests

Airports are important transportation hubs that reside in the heart of modern civilizations.They are of major economic and symbolic value for countries but are thereforealso attractive targets for adversaries. Over the years we have observed successful andunsuccessful terrorist attacks at airports, of which the recent Brussels Airport attack andIstanbul Atatürk Airport attack are two examples.A widely-used method to defend airports against these types of events is that of securityrisk management. Following this approach, security risks are quantified based onthreats, vulnerabilities, and consequences. These risks are then used as a basis to implementsecurity measures that can reduce the risks to acceptable levels. Several securityrisk management approaches were proposed before, such as attack trees and securitygames, but they struggle to include diverse human factors in their analysis. These factorsare inherently present in modern airports, as passengers, employees, and visitors areall humans. Furthermore, existing methods struggle to take other performance metrics,such as efficiency, into account.This thesis addresses these limitations by proposing a novel security risk managementapproach that relies on agent-based models and Monte Carlo simulations. Thisapproach builds on the existing security risk management framework but exploits theadvantages of the agent-based modelling paradigm. Agent-based models allow for theinclusion of rich cognitive, social and organizational models that enable the modellingof human behaviour. Furthermore, agent-based modelling is a suitable paradigm to estimatea variety of performance indicators, including airport efficiency.Two case studieswere performed to assess the performance of our agent-based securityrisk management approach. In these case studies we apply our approach to managesecurity risks at a regional airport, as well as an international airport.

BackgroundA practical and ethical challenge in advance care planning research is controlling and intervening on human behavior. Additionally, observing dynamic changes in advance care planning (ACP) behavior proves difficult, though tracking changes over time is important for intervention development. Agent-based modeling (ABM) allows researchers to integrate complex behavioral data about advance care planning behaviors and thought processes into a controlled environment that is more easily alterable and observable. Literature to date has not addressed how best to motivate individuals, increase facilitators and reduce barriers associated with ACP. We aimed to build an ABM that applies the Transtheoretical Model of behavior change to ACP as a health behavior and accurately reflects: 1) the rates at which individuals complete the process, 2) how individuals respond to barriers, facilitators, and behavioral variables, and 3) the interactions between these variables.MethodsWe developed a dynamic ABM of the ACP decision making process based on the stages of change posited by the Transtheoretical Model. We integrated barriers, facilitators, and other behavioral variables that agents encounter as they move through the process.ResultsWe successfully incorporated ACP barriers, facilitators, and other behavioral variables into our ABM, forming a plausible representation of ACP behavior and decision-making. The resulting distributions across the stages of change replicated those found in the literature, with approximately half of participants in the action-maintenance stage in both the model and the literature.ConclusionsOur ABM is a useful method for representing dynamic social and experiential influences on the ACP decision making process. This model suggests structural interventions, e.g. increasing access to ACP materials in primary care clinics, in addition to improved methods of data collection for behavioral studies, e.g. incorporating longitudinal data to capture behavioral dynamics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-2872-9) contains supplementary material, which is available to authorized users.

Unconventional emergency shows typical unconventionality. Its occurrence, development, evolution mechanism are very complicated, and it may touch off serious derivative disasters. So the emergency management pattern of unconventional emergency has to change from “Predict-Response” to “Scenarios-Response”. This paper summarized and analyzed the typical features of unconventional emergency in three aspects and the current situation and existing problems in emergency management research and practice. Then a “Scenarios-Response” intelligent decision support system (IDSS) framework of unconventional emergency management was established based on Scenario Analysis (SA) and Case-Based Reasoning (CBR). The method of SA provided a detailed and rigorous scenario description for the implementation of unconventional emergency case-based reasoning system; the technology of CBR provided a fast and effective emergency plan supporting for the scenario analysis of unconventional emergency. The IDSS of unconventional emergency management based on SA and CBR could predict accurately the scenarios of unconventional emergency, and provide support and reference for achieving rapid and timely decisions-making and emergency rescue, and it has great significance of establishing and promoting the National Emergency Management System.

To improve the management of discharge pollutants loads in the reservoirs’ watershed, an approach of the allowable pollutants loads calculation and its allocation, based on the water environment model, was proposed. Establishment of the approach framework was described at first. Under the guidance of this framework, two major steps were as follows: modeling and scenario analysis were involved and should be applied to support the decision of discharge loads management; Environmental Fluid Dynamic Code (EFDC) model was selected as the kernel model in this framework. In modeling step, spatial discretization for establishing cell map in model, data preprocessing, parameter calibration and uncertainty analysis (which is considered as the significantly relevant factor of the margin of safety (MOS)), were conducted. As a result of the research, the model-based approach presented as a combination of estimation and precise calculation, which contributed to scenario analysis step. Some integrated modules, such as scenario simulation, result analysis and plan optimization were implemented as cycles in the scenario analysis. Finally, allowable pollutant loads under various conditions were calculated. The Chaihe Reservoir in Liaoning Province, China was used as a case study for an application of the approach described above. Results of the Chaihe reservoir water quality simulation, show good agreement with field data and demonstrated that the approach used in the present study provide an efficient and appropriate methodology for pollutant load allocation.

The modern global supply chains are more vulnerable to disruptions than ever due to their complex geographically distributed designs. This in-depth research evaluates three advanced analytical tools, scenario analysis, Monte Carlo simulation, and predictive analytics, and their application to the problem of supply chain disruption to prevent its development into an operational crisis. This study shows how organizations use these methods to quantify the disruption probabilities, vulnerability exposure and mitigation strategies by using systematic literature review, quantitative modeling, and empirical case analysis. Monte Carlo simulation can be used to give probabilistic quantification of risks in multi-tier supply networks and, through this approach, produce probability distributions of possible outcomes in the thousands of possible outcomes. Scenario analysis allows the strategic assessment of the possible disruption pathways based on systematic exploration of the possible what-if. Using machine learning algorithms and patterns of historical data, predictive analytics provides real-time functionality of risk detection. Findings have shown that hybrid methods which incorporate both these methods can have high predictive accuracy (76-86%), which is better than single-method methods. The analysis of the applications in the manufacturing, healthcare, and food supply chain shows that there are sector-related profiles of risks and mitigation needs. Results indicate the sensitivity of the lower-level visibility of suppliers since the disturbance of Tier 3 is transmitted upstream through supply chains with exponential impacts, worsening the average performance of 97.8% at the origin to 51.1% at the manufacturer stage. The study adds an elaborate framework of unifying these methodologies into organizational risk management procedures that would give the practitioners realistic guidelines to apply in implementing data-driven disruption prevention measures. This research study contributes to the theory on supply chain risk management by showing how quantitative modeling methodologies can convert reactive approaches to crises management to proactive resilience.