Economics & Socio-Ecology  (ES) Session 1

Abstract: The objective of our research is to investigate how an early career research funding enhances the output of National Research Foundation (NRF) Fellows by studying their co-authorships networks and visualizing emerging topics of research of the NRF 2008 cohort through topic modeling of their paper abstracts. NRF Fellowship offers substantial grants worth up to S$3 million (~ € 1.9 M) over 5 years and is open to international applications without restriction on nationality. Since 2008, NRF Fellowship has attracted over a thousand top scientists and awarded nearly a hundred fellowships to date. We analyze collaboration patterns by performing network analysis on the 2008 NRF cohort. In addition, we study network influence and robustness by examining the graph density and degree distribution of co-authorship networks. Collaboration patterns are also examined at a geographic level to understand if the research collaborations are local in nature or distributed at a global level. Lastly, topic modeling with Latent Dirichlet Allocation (LDA) is used as a potential tool for identifying emerging topics of research so that research funding agencies can better support these areas. Increased density of collaborations can be clearly observed for NRF Fellows from co-authorship network graphs during the Fellowship period. Geographical diversity of collaboration for Fellows is also higher than those Finalists (who did not get the award) as evidenced through Circos visualization. Lastly, a visualization of topic models shows that NRF fellows are conducting research on emerging topics from genetic engineering to graphene. Traditionally, bibliometric methods have been utilized to measure the output of researchers. We have used alternative methods from Network theory and Computer Science to analyze the dividends of an early career research funding program leading to greater cross-border academic collaborations and research on emerging topics that may lead to critical breakthroughs for future industries. Close

Abstract: The Anthropocene is thought of as the age of the humans, where in the context of a World-Earth [or social-ecological Earth] system, man-made processes and environmental dynamics can not be treated separately anymore. It is at the core of human agency, to keep the trajectory of this coupled system within the boundaries of a save and just operating space, to ensure prosperity for future generations. In all the common business-as-usual scenarios of future economic development and greenhouse gas emissions, this is not likely to happen. Therefore, we aim at investigating sustainability transitions towards independence from fossil resources. The German Energiewende has proven that besides economic realities, social dynamics such as opinion spreading can play a significant role in the choice of energy sources and thereby resource dependence of a society. Consequently, we study the co-dependence and co-evolution of social, economic and resource dynamic processes. Since we are interested in qualitative behavior of this complex system such as transient behavior and phase transitions rather than quantitative predictions, we use a conceptual model for our studies. This model combines a resource-dependent two-sector economic growth model with heterogeneous households with Fast and Frugal heuristics for household decision making, as well as an adaptive network approach to opinion spreading amongst households. We use analytic and numeric tools to analyze the models state space, to gain insight in its topological structure and phase transitions depending on parameter choices and to point out trajectories that lead to favorable resource dependencies. Close

Abstract: Nestedness is a feature of many bipartite interaction networks found in nature and social sciences. In nested bipartite networks, with two sets of nodes and edges only between nodes belonging to different sets, specialist nodes, namely those with fewer interactions, interact mainly with generalist nodes. Similar structures arise in trade networks as well (e.g. countries-products bipartite networks) and their study is the main focus of the relatively new field of Economic Complexity. Both in ecosystems and economics, such structures of interaction result from an evolutive process, and here we show to what extent it is possible to leverage them to infer phylogenetic relations among species, with methods devised in the context of economics. We project the bipartite network into two monopartite ones based on similarity of interactions and consequently filter the network structure to keep only the most important links. We use independently collected phylogenetic data to assess the accuracy of the proposed methods in identifying phylogenetically related species by only using information on their interactions. We compare the proposed methods against standard network approaches in their ability to isolate communities and topological structures of phylogenetically close species. Previous works on mutualistic interaction networks have already pointed out correlations between degree sequencies and phylogenetic information. Here we show that in most cases the methods we use are able to extract information that is lost on randomized copies of the interaction networks where only the degree sequence is conserved. Close

Abstract: In this paper we analyse the transition towards a circular economy as a complex adaptive system focusing on the contribution of underlying demand-side factors. To do this a heterogeneous agent model, HAM (Brock and Hommes, 1997) is developed with a population of boundely rational heterogeneous agents choosing between two varieties of a consumer goods or service – a 'circular' and a 'non-circular' type. The model is further extended to include the effect of ‘Word of mouth’ by linking the HAM to a percolation model (Frenken et.al, 2012). The model is highly nonlinear due to evolutionary switching between strategies and can exhibit a wide range of dynamical behavior ranging from a unique stable steady state to complex state dynamics with multiple equilibria. For which there can be changes in the qualitative structure of the set of solutions with completely different economic outcomes if parameters are varied. For a classification of these outcomes based on different value of parameters we use bifurcation analysis. Using this analysis we seek to answer the following question: what are the necessary behavioral and market conditions for obtaining a stable market share of the 'circular' type? An Environmental Extended Input-Output database is used to calculate some resource-efficiency indicators for these outcomes. The results and methods developed in this paper is applied to a case of second life of tires in the Netherlands. Close

Abstract: We present a growing network model to explain the visitors' behavior in a tourism destination. Specifically, we build an evolving bipartite network with two categories of nodes, lodgings (H) and services/attractions (S). In every lodging, we assume a sole tourist whose behavior is the average of all tourist's behavior hosted in the lodging. A link between a lodging and service appears if the representative tourist visits/enjoys the service during his/her staying in the destination. We assume that links are unweighted, undirected and permanent along time. The latter assumption means that, once a service is visited by a high enough amount of tourists staying in a certain lodging, the preference for this service is maintained by successive guests. The bipartite network grows similarly to previous models for collaboration networks (e.g. Ramasco et al., Phys. Rev. E, 70, 036106, 2004). At any time, one new lodging and m new services are created in the destination. We assume that the representative tourist of all new hotels visit c different services, including the old and m new ones, following part of them a preferential attachment and the other part a random rule. We show analytically that the long-term degree distribution of services in the bipartite network follows a shifted power-law distribution. This is also the case for the degree of the one-mode projections. We have also tested the model with real data. Specifically we have collected recommendations of lodgings and services in the destination of Maspalomas-Gran Canaria (Spain) published by tourists in tripadvisor.com during the period 2005-2016, with a sample size of around 78.000 opinions on 222 hotels and 768 services/attractions. To the extent of our knowledge, this is the first growing network model to represent the structure of supply and demand in a tourist destination. Close