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The purpose of this chapter is to compile the expected scenarios of development of digital economy in modern Russia and determine the essence and peculiarities of the optimal scenario implementation.

The research is based on the Theory of Games, which is used for comparison of expected scenarios of development of digital economy in modern Russia. A criterion of optimality of the scenario of development of digital economy in modern Russia in this work is effectiveness of its implementation, determined by comparing the results and expenditures in view of probability of each possible sub-scenario.

The performed scenario analysis of development of digital economy in modern Russia showed that the most effective and, therefore, optimal scenario is the one that envisages implementation of the offered new model of a well-balanced digital economy. Despite the fact that probability was determined only for sub-scenarios, within each distinguished scenario, (for determining confidence intervals of the values of indicators) which were not compared with the level of their probability, the given optimal scenario envisages the largest changes compared to the current set course of the formation of digital economy in Russia and hence is the least probable.

The established optimal expected scenario of development of digital economy, which envisages application of its new well-balanced model, is recommended for practical implementation in modern Russia. The given quantitative characteristics of the optimal scenario of development of digital economy in modern Russia could and should be recommended for usage as the basis for developing practical recommendations for monitoring and control of implementation of the optimization model of digital economy in modern Russia.

The purpose of this chapter is to compile the expected scenarios of development of information economy in the global economic system, determine the role of scenario that envisages practical implementation of the new model of a well-balanced information economy among these scenarios according to the criterion of effectiveness and probability, and select and substantiate the optimal scenario.

Due to the absence of initial statistical information that allows characterizing the basic conditions for further development of information economy in the global economic system, the method of stochastic modeling is used for describing forecast scenarios in this chapter. This method is used due to its applicability for forecast analysis in the conditions of uncertainty. With the help of this method, qualitative description of the expected scenarios of development of information economy in the global economic system is accompanied by selected quantitative characteristics, which are conventional, that is, they are given for comparing various scenarios, not for specifying a precise value of estimate indicators. Comparison of the expected scenarios of development of information economy in the global economic system is conducted using comparative analysis method.

It is concluded that the most effective and optimal scenario is the one that supposes implementation of the offered new model of a well-balanced information economy – despite its lower probability as compared to the other scenarios.

The set optimal expected scenario of development of information economy, which envisages application of its new well-balanced model, is recommended for practical implementation in the modern economic systems.

Organizations face challenges in volatile, uncertain, complex and ambiguous (VUCA) environments. It is vital to manage the change’s rate and magnitude in new and different ways to stay competitive. This study focuses on the phenomenon of scenario planning that can help organizations proactively plan for, react and adapt to VUCA forces if and when they occur.

Based on an extensive academic and practitioner literature review, we posit that corporate scenario planning involves eight different practical applications and associated benefits. These include risk identification, assessing uncertainty, organizational learning, options analysis, strategy validation and testing, complex decision-making, strategic nimbleness and innovation. We offer a novel typology and propose a more complete and holistic model of the scenario planning application and its intended outcomes. Mini-case studies from various sectors illustrate the process. The model demonstrates the relationship between different benefit-driven applications - inputs, process and output benefits – and identifies opportunities for further research.

A previous typology study classified “what” and “why” related scenario planning research and literature. However, the why or associated benefits were not broken down at any level of detail, representing a gap in explaining the actual value of this management tool. The current study proposes a novel “why” focused typology of scenario planning benefits based on an extensive literature review. The novel typology adorned several benefits of scenario planning in an integrated model explained using systems theory. These benefits included risk, uncertainty, options analysis, strategic flexibility, complex decision-making, strategy testing and validation, innovation and organizational learning.

First time in the literature, the relationship between input, process and output benefits of scenario planning is explained using systems theory. The novel typology proposed illustrates the practical applications of scenario planning in one complete model.

Occupant behavior can lead to considerable uncertainties in thermal comfort and air quality within buildings. To tackle this challenge, the use of probabilistic controls to simulate occupant behavior has emerged as a potential solution. This study seeks to analyze the performance of free-running households by examining adaptive thermal comfort and CO2 concentration, both crucial variables in indoor air quality. The investigation of indoor environment dynamics caused by the occupants' behavior, especially after the COVID-19 pandemic, became increasingly important. Specifically, it investigates 13 distinct window and shading control strategies in courtyard houses to identify the factors that prompt occupants to interact with shading and windows and determine which control approach effectively minimizes the performance gap.

This paper compares commonly used deterministic and probabilistic control functions and their effects on occupant comfort and indoor air quality in four zones surrounding a courtyard. The zones are differentiated by windows facing the courtyard. The study utilizes the energy management system (EMS) functionality of EnergyPlus within an algorithmic interface called Ladybug Tools. By modifying geometrical dimensions, orientation, window-to-wall ratio (WWR) and window operable fraction, a total of 465 cases are analyzed to identify effective control scenarios. According to the literature, these factors were selected because of their potential significant impact on occupants’ thermal comfort and indoor air quality, in addition to the natural ventilation flow rate. Additionally, the Random Forest algorithm is employed to estimate the individual impact of each control scenario on indoor thermal comfort and air quality metrics, including operative temperature and CO2 concentration.

The findings of the study confirmed that both deterministic and probabilistic window control algorithms were effective in reducing thermal discomfort hours, with reductions of 56.7 and 41.1%, respectively. Deterministic shading controls resulted in a reduction of 18.5%. Implementing the window control strategies led to a significant decrease of 87.8% in indoor CO2 concentration. The sensitivity analysis revealed that outdoor temperature exhibited the strongest positive correlation with indoor operative temperature while showing a negative correlation with indoor CO2 concentration. Furthermore, zone orientation and length were identified as the most influential design variables in achieving the desired performance outcomes.

It’s important to acknowledge the limitations of this study. Firstly, the potential impact of air circulation through the central zone was not considered. Secondly, the investigated control scenarios may have different impacts on air-conditioned buildings, especially when considering energy consumption. Thirdly, the study heavily relied on simulation tools and algorithms, which may limit its real-world applicability. The accuracy of the simulations depends on the quality of the input data and the assumptions made in the models. Fourthly, the case study is hypothetical in nature to be able to compare different control scenarios and their implications. Lastly, the comparative analysis was limited to a specific climate, which may restrict the generalizability of the findings in different climates.

Occupant behavior represents a significant source of uncertainty, particularly during the early stages of design. This study aims to offer a comparative analysis of various deterministic and probabilistic control scenarios that are based on occupant behavior. The study evaluates the effectiveness and validity of these proposed control scenarios, providing valuable insights for design decision-making.

This article shows how scenarios provide a powerful methodology to identify, connect, and assess the critical strategic and knowledge uncertainties they inevitably contain within them, the seeds of any organization’s future marketplace opportunities. Although these two types of uncertainties are quite distinct, scenarios furnish a proven means for senior managers and others to surface and grapple with these uncertainties as a means not just to learn about plausible futures that may confront them but also to better understand the current and emerging competitive context. The outcome is the capacity to upgrade the quality of the inputs to all phases of strategy development and execution.

Some classes of scenario planning tools and techniques are designed to inform near‐term strategic decisions. In addition, some of these are more appropriate for lower levels of uncertainty, while others are best suited for highly uncertain, truly ambiguous business environments. This paper provides a typology of scenario planning tools and techniques that enables managers to identify the best approach to follow in various circumstances. By choosing the right scenario‐planning tool for near‐term strategy decisions, companies can generate the foresight necessary to prevail in today’s turbulent markets. Though most scenario exercises are intended to enrich an organization’s awareness of potential environmental discontinuity in the long‐term, some classes of scenario planning tools and techniques are designed to inform near‐term strategic decisions. Some of these tools for assessing the near term are more appropriate for lower levels of uncertainty, and others are best suited for highly uncertain, truly ambiguous business environments. This paper provides a typology of scenario planning tools and techniques that enables managers to identify the best approach to follow in various circumstances. By choosing the right scenario‐planning tool for near‐term strategy decisions, managers can generate the foresight necessary to help their organization prevail in today’s turbulent markets.

This paper serves to discuss the benefit of applying scenario‐planning techniques to more traditional approaches to strategy development in helping firms deal with uncertainty and evolutionary change in their surrounding environment. Assessing this environment provides insight into the unique changes, the implications these changes have on a firm's strategies, and the creation of special techniques required to understand them. One of these special techniques is scenario planning. Any study of the relationship between strategy, environment and performance must include variables that involve business scope and resource commitments.

A review of the research literature on the use and benefits of both the traditional approaches and the scenario approach to constructing the future strategy of a firm in a changing environment is undertaken.

The results suggest that a better understanding of the performance of firms within a changed environment can be achieved using the combination of a PESTEL analysis, internal resource analysis and the use of scenarios. This reformulation equation creates a model of a possible environment in which the firm must operate and an investigation of the strategic implications of various scenarios to the firm.

This paper is unique in that it marries the traditional approaches to strategy development with the application of scenario planning. It is of benefit to managers and strategic planners by illustrating how a firm may better develop insight into how it should formulate and implement its strategy in order to retain or create a competitive advantage in the changed environment in which it operates.

Transaction monitoring system set up by financial institutions is one of the most used ways to track money laundering and terrorist financing activities. While being effective to a large extent, the system generates very high false positives. With evolving patterns of financial transactions, it also needs effective mechanism for scenario fine-tuning. The purpose of this paper is to highlight quantitative method for optimizing scenarios in money laundering context. While anomaly detection and unsupervised learning can identify huge patterns of false negatives, that can reveal new patterns, for existing scenarios, business generally rely on judgment/data analysis-based threshold finetuning of existing scenario. The objective of such exercises is productivity rate enhancement.

In this paper, the authors propose an approach called linear/non-linear optimization on threshold finetuning. This traditional operations research technique has been often used for many optimization problems. Current problem of threshold finetuning for scenario has two key features that warrant linear optimization. First, scenario-based suspicious transaction reporting (STR) cases and overall customer level catch rate has a very high overlap, i.e. more than one scenario captures same customer with different degree of abnormal behavior. This implies that scenarios can be better coordinated to catch more non-overlapping customers. Second, different customer segments have differing degree of transaction behavior; hence, segmenting and then reducing slack (redundant catch of suspect) can result in better productivity rate (defined as productive alerts divided by total alerts) in a money laundering context.

Theresults show that by implementing the optimization technique, the productivity rate can be improved. This is done through two drivers. First, the team gets to know the best possible combination of threshold across scenarios for maximizing the STR observations better coverage of STR – fine-tuned thresholds are able to better cover the suspected transactions as compared to traditional approaches. Second, there is reduction of redundancy/slack margins on thresholds, thereby improving the overall productivity rate. The experiments focused on six scenario combinations, resulted in reduction of 5.4% of alerts and 1.6% of unique customers for same number of STR capture.

The authors propose an approach called linear/non-linear optimization on threshold finetuning, as very little work is done on optimizing scenarios itself, which is the most widely used practice to monitor enterprise-wide anti-money laundering solutions. This proves that by adding a layer of mathematical optimization, financial institutions can additionally save few million dollars, without compromising on their STR capture capability. This hopefully will go a long way in leveraging artificial intelligence for further making financial institutions more efficient in controlling financial crimes and save some hard-earned dollars.