Displaying 9 of 9 results for 'No%C3%A9 Guiraud'
Analytical Sociology; Social Mechanisms; ABMs; Opinion Dynamics
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Alma Mater: FT Ranked No. 10 Business Economics school.
Ranked No 1 in an engineering mathematics national level test.
Ranked No 1 in an analytics program at IIT Bombay.
B.E. Mechanical Engineering.
MTech 1st year Modelling and Simulation.
PhD 1st year Strategy Simulation at The University of Texas at Dallas.
Tuition scholarships at the Santa Fe Institute.
GMAT 730
5 years of operations research work experience.
Published and presented a poster at the The Operational Research Society, UK Annual Conference 2021 integrating strategy and applied math. Took on and resolved a longstanding problem.
Solo authored leadership article in the Analytics magazine Nov/Dec 2021 issue from INFORMS.
Solo authored theoretical optimization abstract at the ICORES 2022 Conference.
Authoring the black-tie, board room manual - The Change Management Series Volume 1 Kindle edition on Amazon March, 2022.
I am a participant at the Financial Modeling World Cup 2022.
Build spiders for scraping web data.
Agent-based computer simulation in strategy, the resource-based view in strategy, agency theory and top & middle management incentives, organizational economics, algorithmic game theory, financial friction, financial econometrics.
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Guido Fioretti, born 1964, graduated in Electronic Engineering in 1991 at La Sapienza University, Rome. In 1995, he received a PhD in Economics from this same university. Guido Fioretti is currently a lecturer of Organization Science at the University of Bologna.
I am interested in combining social with cognitive sciences in order to model decision-making facing uncertainty. I am particularly interested in connectionist models of individual and organizational decision-making.
I may make use of agent-based models, statistical network analysis, neural networks, evidence theory, cognitive maps as well as qualitative research, with no preference for any particular method. I dislike theoretical equilibrium models and empirical research based on testing obvious hypotheses.
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After being the economic development officer for the Little/Salmon Carmacks First Nation, Tim used all his spare time trying to determine a practical understanding of the events he witnessed. This led him to complexity, specifically human emergent behaviour and the evolutionary prerequisites present in human society. These prerequisites predicted many of the apparently immutable ‘modern problems’ in society. First, he tried disseminating the knowledge in popular book form, but that failed – three times. He decided to obtain PhD to make his ‘voice’ louder. He chose sociology, poorly as it turns out as he was told his research had ‘no academic value whatsoever’. After being forced out of University, he taught himself agent-based modelling to demonstrate his ideas and published his first peer-reviewed paper without affiliation while working as a warehouse labourer. Subsequently, he managed to interest Steve Keen in his ideas and his second attempt at a PhD succeeded. His most recent work involves understanding the basic forces generated by trade in a complex system. He is most interested in how the empirically present evolutionary prerequisites impact market patterns.
Economics, society, complexity, systems, ecosystem, thermodynamics, agent-based modelling, emergent behaviour, evolution.
Two themes unite my research: a commitment to methodological creativity and innovation as expressed in my work with computational social sciences, and an interest in the political economy of “globalization,” particularly its implications for the ontological claims of international relations theory.
I have demonstrated how the methods of computational social sciences can model bargaining and social choice problems for which traditional game theory has found only indeterminate and multiple equilibria. My June 2008 article in International Studies Quarterly (“Coordination in Large Numbers,” vol. 52, no. 2) illustrates that, contrary to the expectation of collective action theory, large groups may enjoy informational advantages that allow players with incomplete information to solve difficult three-choice coordination games. I extend this analysis in my 2009 paper at the International Studies Association annual convention, in which I apply ideas from evolutionary game theory to model learning processes among players faced with coordination and commitment problems. Currently I am extending this research to include social network theory as a means of modeling explicitly the patterns of interaction in large-n (i.e. greater than two) player coordination and cooperation games. I argue in my paper at the 2009 American Political Science Association annual convention that computational social science—the synthesis of agent-based modeling, social network analysis and evolutionary game theory—empowers scholars to analyze a broad range of previously indeterminate bargaining problems. I also argue this synthesis gives researchers purchase on two of the central debates in international political economy scholarship. By modeling explicitly processes of preference formation, computational social science moves beyond the rational actor model and endogenizes the processes of learning that constructivists have identified as essential to understanding change in the international system. This focus on the micro foundations of international political economy in turn allows researchers to understand how social structural features emerge and constrain actor choices. Computational social science thus allows IPE to formalize and generalize our understandings of mutual constitution and systemic change, an observation that explains the paradoxical interest of constructivists like Ian Lustick and Matthew Hoffmann in the formal methods of computational social science. Currently I am writing a manuscript that develops these ideas and applies them to several challenges of globalization: developing institutions to manage common pool resources; reforming capital adequacy standards for banks; and understanding cascading failures in global networks.
While computational social science increasingly informs my research, I have also contributed to debates about the epistemological claims of computational social science. My chapter with James N. Rosenau in Complexity in World Politics (ed. by Neil E. Harrison, SUNY Press 2006) argues that agent-based modeling suffers from underdeveloped and hidden epistemological and ontological commitments. On a more light-hearted note, my article in PS: Political Science and Politics (“Clocks, Not Dartboards,” vol. 39, no. 3, July 2006) discusses problems with pseudo-random number generators and illustrates how they can surprise unsuspecting teachers and researchers.
I am an agent-based simulation modeler and social scientist living near Cambridge, UK.
In recent years, I have developed supply chain models for Durham University (Department of Anthropology), epidemiological models for the Covid-19 pandemic, and agent-based land-use models with Geography PhD students at Cambridge University.
Previously, I spent three years at Ludwig-Maximillians University, Munich, working on Human-Environment Relations and Sustainability, and over two and a half years at Surrey University, working on Innovation with Nigel Gilbert in the Centre for Research in Social Simulation (CRESS). The project at Surrey resulted in a book in 2014, “Simulating Innovation: Computer-based Tools for Rethinking Innovation”. My PhD topic, modeling human agents who energise or de-energise each other in social interactions, drew upon the work of sociologist Randall Collins. My multi-disciplinary background includes degrees in Operational Research (MSc) and Philosophy (BA/MA).
I got hooked on agent-based modeling and complexity science some time around 2000, via the work of Brian Arthur, Stuart Kauffman, Robert Axelrod and Duncan Watts (no relation!).
As an agent-based modeler, I specialize in NetLogo. For data analysis, I use Excel/VBA, and R, and occasionally Python 3, and Octave / MatLab.
My recent interests include:
* conflict and the emergence of dominant groups (in collaboration with S. M. Amadae, University of Helsinki);
* simulating innovation / novelty, context-dependency, and the Frame Problem.
When not working on simulations, I’m probably talking Philosophy with one of the research seminars based in Cambridge. I have a particular interests when these meet my agent-based modeling interests, including:
* Social Epistemology / Collective Intelligence;
* Phenomenology / Frame Problem / Context / Post-Heideggerian A.I.;
* History of Cybernetics & Society.
If you’re based near Cambridge and have an idea for a modeling project, then, for the cost of a coffee / beer, I’m always willing to offer advice.