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on Network Economics |
| By: | M. S. Tedesco; M. A. Nunez-Ochoa; F. Ramos; O. Medrano; K Beuchot |
| Abstract: | The benefits of using complex network analysis (CNA) to study complex systems, such as an economy, have become increasingly evident in recent years. However, the lack of a single comparative index that encompasses the overall wellness of a structure can hinder the simultaneous analysis of multiple ecosystems. A formula to evaluate the structure of an economic ecosystem is proposed here, implementing a mathematical approach based on CNA metrics to construct a comparative measure that reflects the collaboration dynamics and its resultant structure. This measure provides the relevant actors with an enhanced sense of the social dynamics of an economic ecosystem, whether related to business, innovation, or entrepreneurship. Available graph metrics were analysed, and 14 different formulas were developed. The efficiency of these formulas was evaluated on real networks from 11 different innovation-driven entrepreneurial economic ecosystems in six countries from Latin America and Europe and on 800 random graphs simulating similarly constructed networks. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.04346&r= |
| By: | Resch, Christian |
| Abstract: | Online communities offer great potential for sourcing future innovations. While organizations search for inspiration and innovations outside their organizational boundaries to stay competitive, individuals innovate to solve their own needs and subsequently freely reveal these innovations. Online communities constitute a virtual space for individuals to share ideas, socially interact, collaborate, and build on others’ ideas. In this dissertation, I investigate how these social interactions influence the generation of ideas and the ongoing idea development in online communities. The three studies of this dissertation use two unique large datasets that allowed the investigation of social interactions and their contents. In doing so, topic modeling and social network analysis techniques build the methodical foundation to measure latent content representations of the information that is exchanged in online communities. Regarding the generation of new ideas, this dissertation includes two empirical studies that focus on the content that individuals access through their social peers. The first study reveals that the combination of redundant and non-redundant information favors idea newness. In particular, brokers accessing diverse social information benefit from redundant content for generating new ideas. In contrast, non-redundant contents have detrimental effects on brokers’ social non- redundancy regarding brokers’ idea newness. The second study takes a time-dependent view on social interactions and finds that a temporal separation between inspiration and focus on specific contents leads to more innovative outcomes of individuals engaging and innovating in online communities. By focusing on the ongoing collaborative idea development process in online communities, the third study investigates how social influences shape the trajectory ideas take after they got initially shared. The findings of the third study show that social impact theory helps explain how social influences affect the development directions of ideas in online communities. By taking different perspectives on innovative endeavors in online communities, this dissertation contributes to the literature on online communities, social networks, and user innovation. Specifically, this dissertation emphasizes the importance of social interactions for innovations and this relationships’ dependence on the actual content, timing, and social impact of social interactions. |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:133519&r= |
| By: | Christoph Aymanns (London School of Economics & Political Science (LSE) - London School of Economics; University of St. Gallen - School of Finance); Jakob Foerster (University of Oxford); Co-Pierre Georg (University of Cape Town; Deutsche Bundesbank); Matthias Weber (University of St. Gallen - School of Finance; Swiss Finance Institute) |
| Abstract: | We propose multi-agent reinforcement learning as a new method for modeling fake news in social networks. This method allows us to model human behavior in social networks both in unaccustomed populations and in populations that have adapted to the presence of fake news. In particular the latter is challenging for existing methods. We find that a fake-news attack is more effective if it targets highly connected people and people with weaker private information. Attacks are more effective when the disinformation is spread across several agents than when the disinformation is concentrated with more intensity on fewer agents. Furthermore, fake news spread less well in balanced networks than in clustered networks. We test a part of these findings in a human-subject experiment. The experimental evidence provides support for the predictions from the model. This suggests that our model is suitable to analyze the spread of fake news in social networks. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2258&r= |
| By: | Köppl-Turyna, Monika; Köppl, Stefan; Christopulos, Dimitris |
| Abstract: | In this paper we analyze how different types of venture capital investments - private, public and indirect public - affect performance of portfolio companies. We use data on more than 20,000 VC deals in Europe between 2000 and 2018 and we hand collected a unique dataset on the institutional setting (public/indirect/private) of almost 5000 investors. We find that public VC investors perform consistently worse than purely private ones, while indirect public investments (such as the "Juncker Plan" or InvestEU investments) perform consistently better. We link these findings to the fact that public funds do not enter the best performing cliques of investments. On the other hand, indirect funds invest in the VC funds with the best network characteristics, which raises a question of whether indirect VC investments are associated with a high level of windfall gain, and not necessarily improve the value added by the VC funds. We confirm the main conclusions using instrumental variables' specifications. |
| Keywords: | venture capital,network analysis,governmental venture capital,European Investment Fund,syndication,public policy |
| JEL: | G24 G28 H81 L26 D73 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ecoarp:21&r= |
| By: | Zeyi Chen |
| Abstract: | The model of Network Coloring Game (NCG) is used to simulate conflict resolving and consensus reaching procedures in social science. In this work, we adopted some Markov Chain Techniques into the investigation of NCG. Firstly, with no less than $\Delta + 2$ colors provided, we proposed and proved that the conflict resolving time has its expectation to be $O(\log n)$ and the variance $O((\log n)^2)$, thus is $O_p(\log n)$, where $n$ is the number of vertices and $\Delta$ is the maximum degree of the network. This was done by introducing an absorbing Markov Chain into NCG. Secondly, we developed algorithms to reduce the network in post-conflict-resolution adjustments when a Borda rule is applied among players. Markov Chain Monte Carlo methods were employed to estimate both local and global optimal payoffs. Supporting experimental results were given to illustrate the corresponding procedures. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.09153&r= |
| By: | Luca Paolo Merlino; Nicole Tabasso |
| Abstract: | We study the diffusion of a true and a false message when agents are (i) biased towards one of the messages and (ii) agents are able to inspect messages for veracity. Inspection of messages implies that a higher rumor prevalence may increase the prevalence of the truth. We employ this result to discuss how a planner may optimally choose information inspection rates of the population. We find that a planner who aims to maximize the prevalence of the truth may find it optimal to allow rumors to circulate. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.01830&r= |