Optimal diversification strategies in the networks of related products and of related research areas

Abstract

Countries and cities are likely to enter economic activities that are related to those that are already present in them. Yet, while these path dependencies are universally acknowledged, we lack an understanding of the diversification strategies that can optimally balance the development of related and unrelated activities. Here, we develop algorithms to identify the activities that are optimal to target at each time step. We find that the strategies that minimize the total time needed to diversify an economy target highly connected activities during a narrow and specific time window. We compare the strategies suggested by our model with the strategies followed by countries in the diversification of their exports and research activities, finding that countries follow strategies that are close to the ones suggested by the model. These findings add to our understanding of economic diversification and also to our general understanding of diffusion in networks.

Resisting Influence: How the Strength of Predispositions to Resist Control Can Change Strategies for Optimal Opinion Control in the Voter Model

“…This contrasts with simple contagion (modeled, for example, by independent cascades), in which similarly to disease spreading, only one contact is required to spread a message. An immediate consequence of such different dynamics is that hubs typically represent the best influencer under a simple contagion dynamics, whereas targeting low-degree nodes may yield a larger spread for complex contagion (Alshamsi et al, 2017). By including predispositions to resist change in our model nodes can spontaneously revert to the uninfluenced state.…”

“…Therefore, the proposed model reflects the repeated exposure needed in complex contagion to influence a node with high probability. Alshamsi et al (2017) has recently shown that it may be best to influence low-degree nodes in complex contagion in a setting in which nodes are committed to a state once adopted. Our results complement these findings in dynamic settings and show further conditions under which it is best to target low-degree nodes instead of hub nodes.…”

Computational aspects of optimal strategic network diffusion

“…As finding an optimal solution proves to be computationally demanding, we turn our attention to assessing the possibility of obtaining an efficient approximation algorithm. We first investigate whether two effective heuristic algorithms proposed by Alshamsi et al [1] have constant approximation ratios. One of them is the greedy algorithm, i.e., always targeting the node with the highest probability of activation.…”

“…The goal is to activate the entire network, starting from a single active node, in the minimum time possible. While Alshamsi et al [1] showed for a wide range of network topologies that activating a network in an optimal manner requires a balance between exploitation and exploration strategies, many questions regarding the computational feasibility of finding optimal strategies remained open. Here, we explore several theoretical computational considerations of strategic diffusion processes.…”

Better to Stay Apart: Asset Commonality, Bipartite Network Centrality, and Investment Strategies

“…Furthermore, recent publications have also proposed nonlinear versions of the algorithm to measure centrality in bipartite networks; the interested reader can refer e.g. to: Tacchella et al (2012Tacchella et al ( , 2013; Morrison et al (2017); Alshamsi et al (2018), among others. observables:…”