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on Network Economics |
| By: | Krishna Dasaratha; Benjamin Golub; Anant Shah |
| Abstract: | A principal uses payments conditioned on stochastic outcomes of a team project to elicit costly effort from the team members. We develop a multi-agent generalization of a classic first-order approach to contract optimization by leveraging methods from network games. The main results characterize the optimal allocation of incentive pay across agents and outcomes. Incentive optimality requires equalizing, across agents, a product of (i) individual productivity (ii) organizational centrality and (iii) responsiveness to monetary incentives. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08026 |
| By: | Fumihiko Isada (Kansai University) |
| Abstract: | The aim of this study is to empirically identify how the structure of inter-organisational collaboration is changing in today's increasingly digitalised insurance industry.Traditionally, inter-organisational relationships in the insurance industry have been vertically integrated, mainly with major insurance companies. With the advent of digitalisation, the areas covered by insurance, such as disease prevention, are expanding through the linkage of various big data. It can be inferred that the structure of cooperation between related organisations and their core organisations is changing accordingly.As a research method, this study analysed information on actual inter-organisational relations using the method of social network analysis. The linkage network structure of the organisations that are expanding inter-organisational cooperation was then analysed.The results of the analysis show that the organisations that are expanding inter-organisational cooperation have an open and mediated network structure.It was shown that inter-organisational relations in the insurance industry may be shifting from a vertically integrated structure to a platform type, similar to the IT industry. |
| Keywords: | Inter-organisational collaboration, InsurTech, Social network analysis |
| JEL: | M15 O33 I13 |
| URL: | https://d.repec.org/n?u=RePEc:sek:iefpro:14716480 |
| By: | Juite Wang (Graduate Institute of Technology Management, National Chung Hsing University) |
| Abstract: | Innovation ecosystems have become an indispensable element in the growth strategy of firms in various industries. In the birth stage of innovation ecosystem, it is important for firms to assess technological positions of various actors in the innovation ecosystem to support decisions on external R&D collaboration. This research integrates semantic analysis and bibliometric analysis for predicting evolving collaboration patterns and predict collaboration potential. Semantic analysis applies the context-aware deep learning framework based on BERT [14] to analyze unstructured patent data and evaluate technological similarity between individual firms. In addition, biblio-metric analysis uses patent indicators related to technological capabilities and potential technology synergy of individual firms. Then, the deep neural network (DNN) approach is used to learn the relationships between descriptive features and collaboration potentials as target feature. Our findings suggest that the metaverse innovation ecosystem remains in its nascent stages, with the collaborative network still being sparse. The illustrative example reveals that recommended candidate partners often align with or resemble past partners from prior periods. This suggests that the pro-posed deep learning approach is capable of predicting collaborative relationships between various firms. |
| Keywords: | Innovation ecosystems, Deep learning, Collaboration network, Natural language processing |
| URL: | https://d.repec.org/n?u=RePEc:sek:iefpro:14716418 |
| By: | Hanbat Jeong |
| Abstract: | This paper introduces a novel spatial interaction model to explore the decision-making processes of two types of agents-a leader and followers-with central and local governments serving as empirical representations. The model accounts for three key features: (i) resource allocations from the leader to the followers and the resulting strategic interactions, (ii) followers' choices across multiple activities, and (iii) interactions among these activities. We develop a network game to examine the micro-foundations of these processes. In this game, followers engage in multiple activities, while the leader allocates resources by monitoring the externalities arising from followers' interactions. The game's unique NE is the foundation for our econometric framework, providing equilibrium measures to understand the short-term impacts of changes in followers' characteristics and their long-term consequences. To estimate the agent payoff parameters, we employ the QML estimation method and examine the asymptotic properties of the QML estimator to ensure robust statistical inferences. Empirically, we investigate interactions among U.S. states in public welfare expenditures (PWE) and housing and community development expenditures (HCDE), focusing on how federal grants influence these expenditures and the interactions among state governments. Our findings reveal positive spillovers in states' PWEs, complementarity between the two expenditures within states, and negative cross-variable spillovers between them. Additionally, we observe positive effects of federal grants on both expenditures. Counterfactual simulations indicate that federal interventions lead to a 6.46% increase in social welfare by increasing the states' efforts on PWE and HCDE. However, due to the limited flexibility in federal grants, their magnitudes are smaller than the proportion of federal grants within the states' total revenues. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13810 |