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

Artificial Intelligence and International Conflict in Cyberspace

This edited volume explores how artificial intelligence (AI) is transforming international conflict in cyberspace. Over the past three decades, cyberspace developed into a crucial frontier and issue of international conflict. However, scholarly work on the relationship between AI and conflict in cyberspace has been produced along somewhat rigid disciplinary boundaries and an even more rigid sociotechnical divide – wherein technical and social scholarship are seldomly brought into a conversation. This is the first volume to address these themes through a comprehensive and cross-disciplinary approach. With the intent of exploring the question ‘what is at stake with the use of automation in international conflict in cyberspace through AI?’, the chapters in the volume focus on three broad themes, namely: (1) technical and operational, (2) strategic and geopolitical and (3) normative and legal. These also constitute the three parts in which the chapters of this volume are organised, although these thematic sections should not be considered as an analytical or a disciplinary demarcation

Preference elicitation in synthetic social networks

W artykule rozważano scenariusz, w którym administracja publiczna wykorzystuje internetową platformę społecznościową do komunikacji z obywatelami i uzyskiwania informacji o ich preferencjach. Z platformy tej korzysta tylko część całej populacji (subpopulacja), co powoduje, że preferencje obserwowane na platformie mogą być niereprezentatywne dla całego społeczeństwa. W niniejszym opracowaniu uwzględniono dwa problemy związane z brakiem reprezentatywności preferencji, tj.: (1) odmienną strukturę demograficzną populacji i subpopulacji oraz (2) różnice w procesie dynamiki preferencji w całej populacji i subpopulacji wyrażającej swoje opinie na platformie społecznościowej. Dane wykorzystane w analizie obejmują informacje o aktywności użytkowników na platformie społecznościowej, ich dane socjodemograficzne oraz dane o populacji pochodzące ze spisu powszechnego. W celu badania dynamiki preferencji skonstruowano wieloagentowy model symulacyjny, w którym sieć społeczną przedstawiono za pomocą nieskierowanego grafu, gdzie węzły reprezentują obywateli, a łuki ich relacje społeczne. W procesie analizy najpierw jest generowana sztuczna populacja i na niej jest symulowana dynamika preferencji. Następnie losowo, metodą kuli śnieżnej (ang. snowballsampling) są wybierane różne niereprezentatywne subpopulacje, na których są testowane algorytmy uogólniania preferencji przez odtwarzanie dynamiki całej populacji. Miarą jakości modelu jest zgodność preferencji między subpopulacją a całą populacją. Rezultaty przeprowadzonych symulacji wskazały na skuteczność zastosowanej metody: wraz z kolejnymi krokami symulacji wzrasta zgodność między populacją rzeczywistą a syntetyczną. Okazało się również, że najistotniejszymi determinantami błędów uogólniania preferencji są model dyfuzji preferencji oraz waga opinii własnej agenta.The paper considers a scenario in which public administration (PA) uses an online social platform to collect information on citizens' preferences. However, the opinions of the sub-population that uses the online platform might be not representative. The author develops a method for generalization of the dynamics of the preferences observed on the social platform onto the entire population. The available data include information collected by the PA from the online platform (assuming that it is run and administered by the PA) and census data regarding the population. Hence, the PA has access to basic personal data of platform users (e.g. gender and age), position in the online social network, and opinions revealed on the platform. The online users' data can be analyzed along with the aggregated census data on the entire population. The author has implemented a multi-agent simulation model that takes into account the distribution of personal attributes, social network data, and opinion diffusion dynamics. The analysis involves showing how different algorithms enable generalization of preferences collected by the online platform to the entire population. The results of the analysis prove that the proposed method is efficient in the preference elicitation process – with each simulation step, the preference congruence level between real and synthetic populations increases. The main determinants of preference elicitation errors include the preference diffusion model and the weight of the agents’ own opinions.Niniejsze prace badawcze zostały zrealizowane w ramach projektu ROUTE-TO-PA (Raising Open and User-friendly Transparency-Enabling Technologies for Public Administrations) [http://routetopa.eu/], który jest finansowany ze środków Europejskiego Programu w Zakresie Badań Naukowych i Innowacji „Horizon 2020” na podstawie umowy o dotację nr 645860. Autorzy wyrażają również podziękowanie anonimowym recenzentom za ich uwagi dotyczące treści artykułu.Marcin Czupryna: [email protected]ław Szufel: [email protected]ł Kamiński: [email protected] Wiertlewska: [email protected] Marcin Czupryna - Szkoła Główna Handlowa w Warszawiedr Przemysław Szufel - Szkoła Główna Handlowa w Warszawiedr hab. 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