Policy Advice Derived from Simulation Models

When advising policy we face the fundamental problem that economic processes are uncertain. Consequently, policy can err. In this paper we show how the use of simulation models can reduce policy errors by inferring empirically reliable and meaningful statements about economic processes. We suggest that policy is best based on so-called abductive simulation models, which help to better understand how policy measures can influence economic processes. We show that abductive simulation models use a combination of theoretical and empirical analysis based on different data sets. By way of example we show what policy can learn with the help of abductive simulation models, namely how policy measures can influence the emergence of a regional cluster.Policy Advice, Simulation Models, Uncertainty, Methodology

Policy Advice Derived From Simulation Models

When advising policy we face the fundamental problem that economic processes are connected with uncertainty and thus policy can err. In this paper we show how the use of simulation models can reduce policy errors. We suggest that policy is best based on so-called abductive simulation models, which help to better understand how policy measures can influence economic processes. We show that abductive simulation models use a combination of theoretical and empirical analysis based on different data sets. This helps inferring empirically reliable and meaningful statements about how policy measures influence economic processes. By way of example we show how research subsidies by the government influence the likelihood that a regional cluster emerges.Policy Advice, Simulation Models, Uncertainty, Methodology

THE ROLE AND IMPORTANCE OF ACADEMIC FREEDOM IN THE POLICY PROCESS

Academic freedom, Policy process, policy decision-making, policy advice, research, research institute, Political Economy, Teaching/Communication/Extension/Profession,

Сучасні підходи до імітаційного моделювання просторового розвитку регіонів

Перепелюкова, О. В. Сучасні підходи до імітаційного моделювання просторового розвитку регіонів = Modern approaches to the simulative modeling of the spatial development of the regions / О. В. Перепелюкова // Зб. наук. пр. НУК. – Миколаїв : НУК, 2019. – № 4 (478). – С. 45–50.Анотація. У статті проаналізовано основні сучасні підходи до імітаційного моделювання просторового розвитку регіонів. Встановлено, що одним з інструментів вирішення проблем збалансування регіонального розвитку за рахунок виокремлення факторів негативного впливу на просторовий розвиток регіонів є застосування імітаційного моделювання просторового економічного співробітництва. Метою статті є аналіз сучасних підходів до імітаційного моделювання просторового розвитку регіонів. Для вирішення поставленої мети в роботі було використано такі методи: метод аналізу та синтезу; метод групування основних факторів впливу на просторовий розвиток регіонів; метод моделювання; метод побудови імітаційної моделі факторів впливу на просторовий розвиток регіонів. У статті було встановлено, що одним з дієвих інструментів комплексного оцінювання і порівняння рівня просторового розвитку регіонів та його прогнозування можуть виступати інформаційні системи з використанням імітаційних моделей, які дозволяють провести оцінку та прогнозування впливу різних факторів на результати просторового розвитку регіонів. Було визначено, що просторовий розвиток в деякому аспекті також залежить від ефективності використання продуктивних сил. Така система імітаційного моделювання дозволяє вирішити ряд проблем інформаційно-аналітичного забезпечення органів управління різного рівня інформаційними та аналітично-прогнозними ресурсами для розробки та реалізації політики щодо розвитку територій. Основним результатом дослідження є виокремлення факторів впливу на просторовий розвиток регіонів, що за допомогою зміни сили впливу наведених факторів можливо спрогнозувати різні сценарії розвитку просторового співробітництва регіонів. Наведені в роботі рекомендації можуть бути використані для імітаційного моделювання різних сценаріїв розвитку просторового співробітництва регіонів з урахуванням сили впливу кожного фактору, а також його зміна на основі заходів щодо сприяння розвитку регіональної співпраці.Abstract. The article analyzes the basic modern approaches to imitation modeling of spatial development of regions. It is established that one of the tools for solving the problems of balancing regional development by isolating the factors of negative impact on the spatial development of the regions is the use of simulation modeling of spatial economic cooperation. The purpose of the article is to analyze modern approaches to imitation modeling of spatial development of regions. To solve this goal, such methods as analysis and synthesis were used to group the main factors of influence on the spatial development of regions, modeling, and to construct a simulation model of the factors of influence on the spatial development of regions. The article found that one of the effective tools for complex assessment and comparison of the level of spatial development of regions and its forecasting can be information systems using simulation models that allow to evaluate and predict the impact of various factors on the results of spatial development of regions. It has been determined that spatial development in some aspect also depends on the efficiency of the use of productive forces Such a system of imitation modeling allows to solve a number of problems of information and analytical support of different levels of management information and analytical and forecasting resources for the development and implementation of territorial development policy. The main result of the study is the isolation of the factors of influence on the spatial development of the regions, which by changing the power of the influence of the above factors, it is possible to predict different scenarios for the development of the spatial cooperation of the regions. The recommendations given in the paper can be used to simulate different scenarios for the development of spatial cooperation in the regions, taking into account the impact of each factor, as well as changing it based on measures to promote the development of regional cooperation

An Objective-Based Perspective on Assessment of Model-Supported Policy Processes

Simulation models, being in use for a long time in natural sciences and engineering domains, are diffusing to a wider context including policy analysis studies. The differences between the nature of the domain of application, as well as the increased variety of usage partially induced by this difference naturally imply new challenges to be overcome. One of these challenges is related to the assessment of the simulation-based outcomes in terms of their reliability and relevance in the policy context being studied. The importance of this assessment is twofold. First of all, it is all about conducting a high quality policy study with effective results. However, the quality of the study does not necessarily imply acceptance of the results by the clients and/or colleagues. This problem of policy analysts increases the importance of such an assessment; an effective assessment may induce the acceptance of the conclusions drawn from the study by the clients and/or colleagues. The main objective of this paper is to introduce an objective-based assessment perspective for simulation model-supported policy studies. As a first step towards such a goal, an objective-based classification of models is introduced. Based on that, we will discuss the importance of different aspects of the assessment for each type. In doing so, we aim to provide a structured discussion that may serve as a sort of methodological guideline to be used by policy analysts, and also by clients.Simulation, Validation, Model Assessment, Policy Analysis, Model Typology

Regional innovation systems as complex adaptive systems: The case of lagging European regions

This article proposes an agent-based model to support the development of self-sustaining regional innovation systems (RIS). The model is the base of a computational laboratory, CARIS (Complex Adaptive Regional Innovation System), which aims at evaluating the self-sustainability of RIS and at investigating what are the resources, competencies and mechanisms able to trigger powerful innovation and economic growth processes. Such a topic is particularly interesting for the so-called lagging regions, which, notwithstanding noticeable policy interventions, have been unable to significantly improve their innovation performances. Results of this study show that the exploration capacity, the propensity to cooperation, and the endowed competencies of actors belonging to a region could be considered as key aspects in affecting the regional innovation performance. This means that policy-makers should (i) incentivize investments in research and development activities both at the public and private levels; (ii) support public-private partnerships; (iii) enhance national and regional university systems; and (iv) increase the number of researchers employed both in the public and private sectors. In the next future, the CARIS laboratory could be adopted as policy support instrument to evaluate how much effective are current innovation policies and what are the most effective ones to reassess the current patterns

Ecosystemic Evolution Feeded by Smart Systems

Information Society is advancing along a route of ecosystemic evolution. ICT and Internet advancements, together with the progression of the systemic approach for enhancement and application of Smart Systems, are grounding such an evolution. The needed approach is therefore expected to evolve by increasingly fitting into the basic requirements of a significant general enhancement of human and social well-being, within all spheres of life (public, private, professional). This implies enhancing and exploiting the net-living virtual space, to make it a virtuous beneficial integration of the real-life space. Meanwhile, contextual evolution of smart cities is aiming at strongly empowering that ecosystemic approach by enhancing and diffusing net-living benefits over our own lived territory, while also incisively targeting a new stable socio-economic local development, according to social, ecological, and economic sustainability requirements. This territorial focus matches with a new glocal vision, which enables a more effective diffusion of benefits in terms of well-being, thus moderating the current global vision primarily fed by a global-scale market development view. Basic technological advancements have thus to be pursued at the system-level. They include system architecting for virtualization of functions, data integration and sharing, flexible basic service composition, and end-service personalization viability, for the operation and interoperation of smart systems, supporting effective net-living advancements in all application fields. Increasing and basically mandatory importance must also be increasingly reserved for human–technical and social–technical factors, as well as to the associated need of empowering the cross-disciplinary approach for related research and innovation. The prospected eco-systemic impact also implies a social pro-active participation, as well as coping with possible negative effects of net-living in terms of social exclusion and isolation, which require incisive actions for a conformal socio-cultural development. In this concern, speed, continuity, and expected long-term duration of innovation processes, pushed by basic technological advancements, make ecosystemic requirements stricter. This evolution requires also a new approach, targeting development of the needed basic and vocational education for net-living, which is to be considered as an engine for the development of the related ‘new living know-how’, as well as of the conformal ‘new making know-how’

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly