About this Research Topic
The recent vicissitudes generated with the onset of the Covid-19 pandemic, as well as the catastrophic effects that materialized during the Great recession, constantly remind us that interaction in socioeconomic systems is far from being neutral. In whatever way this is modeled, either via interconnectedness among agents, sophisticated network theory tools, or stochastic differential equations, the interaction among entities (for example, relationships between banks and firms, intermediaries, transnational corporations, friends, families) represents the channel to increase/reduce socioeconomic heterogeneity and inequality and to propagate (in)stability. While interaction has historically always led to social change, this process has dramatically sped up in our current highly globalized world, as evident in Social Media, such as Twitter, Facebook, Reddit.
The acceleration, mainly due to ICT (Information and Communications Technology), influences and determines public and private choices, individual behaviors, collective decisions, and real and financial markets. What is ascertained is the existence of (vicious) virtuous relationships reflected in (non)resilient network topologies. Since the network topology mirrors the interaction that generates it, the aggregate can be used to understand-remodel the micro-level and direct individual strategies towards a more stable architecture.
In this Research Topic, we aim to shed light on the relationship between individual behaviors and network architecture to identify the best performing strategies maximizing the resilience of the socioeconomic system. We welcome contributions from all disciplines (particularly Economics, Physics, Social Sciences, Engineering) to provide a quantitative understanding of the interplay between networks and stability and resilience in social systems. We invite authors to submit both data-driven contributions and modeling-oriented studies. Potential topics include but are not limited to the following:
· Bottom-up approaches (such as agent-based models) identifying aggregate stability from micro-interaction.
· Network theory and informative cascades
· Collective decision making
· Community analysis
· Machine/reinforcement learning
· Behavioral finance
· Data-driven behavioral models
· Game theoretical models
· Herding/switching models
Keywords: Network Science, Social Systems, Data-driven Science, Information and Communications Technology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.