Jan Ryckebusch | Ghent University (original) (raw)
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Papers by Jan Ryckebusch
Journal of physics, Oct 12, 2022
Innovation is fundamental for development and provides a competitive advantage for societies. It ... more Innovation is fundamental for development and provides a competitive advantage for societies. It is the process of creating more complex technologies, ideas, or protocols from existing ones. While innovation may be created by single agents (i.e. individuals or organisations), it is often a result of social interactions between agents exchanging and combining complementary expertise and perspectives. The structure of social networks impacts this knowledge exchange process. To study the role of social network structures on the creation of new technologies, we design an evolutionary mechanistic model combining self-creation and social learning. We find that social heterogeneity allows agents to leverage the benefits of diversity and to develop technologies of higher complexity. Social heterogeneity, however, reduces the group ability to innovate. Not only the social structure but also the openness of agents to collaborate affect innovation. We find that interdisciplinary interactions lead to more complex technologies benefiting the entire group but also increase the inequality in the innovation output. Lower openness to interdisciplinary collaborations may be compensated by a higher ability to collaborate with multiple peers, but low openness also neutralises the intrinsic benefits of network heterogeneity. Our findings indicate that social network heterogeneity has contrasting effects on microscopic (local) and macroscopic (group) levels, suggesting that the emergence of innovation leaders may suppress the overall group performance.
Physical Review Letters, Mar 2, 2020
Physical review, Jan 20, 2005
Nucleation and Atmospheric Aerosols, 2011
Physics Letters B, Apr 1, 2018
Physical review, Feb 6, 2019
Physica D: Nonlinear Phenomena, Aug 1, 2022
Physical review, Dec 17, 2013
Journal of Statistical Mechanics: Theory and Experiment, Nov 10, 2016
Physics Letters B, Aug 1, 1987
arXiv (Cornell University), May 25, 2022
Complex networks 2018 : the 7th international conference on complex networks and their applications : book of abstracts, 2018
Bulletin of the American Physical Society, 2020
Recently, much effort has been devoted to studying whether machine learning methods are capable o... more Recently, much effort has been devoted to studying whether machine learning methods are capable of recognizing phase boundaries in spin systems. This is typically done using deep neural networks, trained on system configurations at fixed control parameter values, but without receiving any a priori knowledge of physical features. Opening the ‘black-box algorithms’ and uncovering which features are captured by the neural network is a crucial and oft-overlooked step. Without this additional step, one cannot guarantee that the algorithm's decision on the phase boundaries is based on physically relevant features, or on less relevant characteristics—which would limit its applicability. We use the example of exploring the two-dimensional phase diagram of a non-equilibrium spin system (active Ising model) to highlight the importance of scrutinizing the internal representation of a neural network. By only training networks on a small slice of the phase diagram, we show that some networks...
Political Analysis, 2021
We apply variations and extensions of structural balance theory to analyze the dynamics of geopol... more We apply variations and extensions of structural balance theory to analyze the dynamics of geopolitical relations using data from the virtual world Eve Online. The highly detailed data enable us to study the interplay of alliance size, power, and geographic proximity on the prevalence and conditional behavior of triads built from empirical political alliances. Through our analysis, we reveal the degree to which the behaviors of players conform to the predictions of structural balance theory and whether our augmentations of the theory improve these predictions. In addition to studying the time series of the proportions of triad types, we investigate the conditional changes in triad types and the formation of polarized political coalitions. We find that player behavior largely conforms to the predictions of a multipolar version of structural balance theory that separates strong and weak configurations of balanced and frustrated triads. The high degree of explanatory power of structura...
Physical review, Aug 2, 1999
Journal of physics, Oct 12, 2022
Innovation is fundamental for development and provides a competitive advantage for societies. It ... more Innovation is fundamental for development and provides a competitive advantage for societies. It is the process of creating more complex technologies, ideas, or protocols from existing ones. While innovation may be created by single agents (i.e. individuals or organisations), it is often a result of social interactions between agents exchanging and combining complementary expertise and perspectives. The structure of social networks impacts this knowledge exchange process. To study the role of social network structures on the creation of new technologies, we design an evolutionary mechanistic model combining self-creation and social learning. We find that social heterogeneity allows agents to leverage the benefits of diversity and to develop technologies of higher complexity. Social heterogeneity, however, reduces the group ability to innovate. Not only the social structure but also the openness of agents to collaborate affect innovation. We find that interdisciplinary interactions lead to more complex technologies benefiting the entire group but also increase the inequality in the innovation output. Lower openness to interdisciplinary collaborations may be compensated by a higher ability to collaborate with multiple peers, but low openness also neutralises the intrinsic benefits of network heterogeneity. Our findings indicate that social network heterogeneity has contrasting effects on microscopic (local) and macroscopic (group) levels, suggesting that the emergence of innovation leaders may suppress the overall group performance.
Physical Review Letters, Mar 2, 2020
Physical review, Jan 20, 2005
Nucleation and Atmospheric Aerosols, 2011
Physics Letters B, Apr 1, 2018
Physical review, Feb 6, 2019
Physica D: Nonlinear Phenomena, Aug 1, 2022
Physical review, Dec 17, 2013
Journal of Statistical Mechanics: Theory and Experiment, Nov 10, 2016
Physics Letters B, Aug 1, 1987
arXiv (Cornell University), May 25, 2022
Complex networks 2018 : the 7th international conference on complex networks and their applications : book of abstracts, 2018
Bulletin of the American Physical Society, 2020
Recently, much effort has been devoted to studying whether machine learning methods are capable o... more Recently, much effort has been devoted to studying whether machine learning methods are capable of recognizing phase boundaries in spin systems. This is typically done using deep neural networks, trained on system configurations at fixed control parameter values, but without receiving any a priori knowledge of physical features. Opening the ‘black-box algorithms’ and uncovering which features are captured by the neural network is a crucial and oft-overlooked step. Without this additional step, one cannot guarantee that the algorithm's decision on the phase boundaries is based on physically relevant features, or on less relevant characteristics—which would limit its applicability. We use the example of exploring the two-dimensional phase diagram of a non-equilibrium spin system (active Ising model) to highlight the importance of scrutinizing the internal representation of a neural network. By only training networks on a small slice of the phase diagram, we show that some networks...
Political Analysis, 2021
We apply variations and extensions of structural balance theory to analyze the dynamics of geopol... more We apply variations and extensions of structural balance theory to analyze the dynamics of geopolitical relations using data from the virtual world Eve Online. The highly detailed data enable us to study the interplay of alliance size, power, and geographic proximity on the prevalence and conditional behavior of triads built from empirical political alliances. Through our analysis, we reveal the degree to which the behaviors of players conform to the predictions of structural balance theory and whether our augmentations of the theory improve these predictions. In addition to studying the time series of the proportions of triad types, we investigate the conditional changes in triad types and the formation of polarized political coalitions. We find that player behavior largely conforms to the predictions of a multipolar version of structural balance theory that separates strong and weak configurations of balanced and frustrated triads. The high degree of explanatory power of structura...
Physical review, Aug 2, 1999