Gastón Olivares

In an increasingly interconnected world, the capacity of societies to withstand, adapt to, and recover from crises is a central challenge for security and sustainable development. Yet, despite extensive research on resilience, the mechanisms through which systemic vulnerabilities emerge and propagate across social domains remain poorly understood. This paper addresses this gap by proposing a network-based framework: the Complex Analysis for Socio-Environmental Adaptation (CASA), which models resilience as a graph-structured system. Each node in CASA represents a social or infrastructural component whose resistance is derived from indicators of installed capacities, while edges capture interdependencies among sectors. We formalize a damage propagation model in which the loss of capacity in one node dynamically affects connected components, revealing the topological patterns that drive systemic fragility. Comparative simulations demonstrate that CASA’s topology amplifies the impact of highly connected nodes, rendering them crucial for resilience planning. An application to a real-world case demonstrates how initial disruptions in access to drinking water cascade into governance, economic, and social instabilities. The results provide both theoretical and operational insights, highlighting that resilience depends not only on the strength of individual components but also on the network architecture that links them. CASA thus offers a replicable and data-informed approach for identifying drivers of social conflict and guiding anticipatory resilience strategies in complex territorial systems.

Addressing climate change requires innovative, collective action and robust international collaboration. Through joint efforts, nations can significantly reduce greenhouse gas emissions, pioneer sustainable technologies, and implement effective adaptation measures. Science diplomacy and knowledge sharing hold the potential to bolster global stability and peace by directly confronting climate change challenges. Therefore, it becomes imperative to evaluate a country’s alignment of its scientific knowledge system (SKS) with international guidelines. This study delineates the global scientific discourse on climate change and juxtaposes the alignment between an individual nation’s research endeavors and United Nations resolutions concerning climate change and sustainable development. Our methodology integrates data extraction from scientific research databases with advanced textual analysis tools, highlighting this study’s unique focus on the intersection of climate change and UN resolutions. To deliver an empirical analysis, we leveraged complex network theory and advanced text-processing techniques. Our findings demonstrate the trajectory of global scientific output related to these themes, segmented by countries and coupled with CO₂ emissions data, key disciplines, and collaboration networks. These insights are instrumental for leaders, policymakers, and stakeholders, highlighting areas of convergence and divergence in national research initiatives essential for achieving global climate goals. Such knowledge is strategically useful for crafting purpose-driven public policies and honoring enduring multilateral pledges to address the climate crisis proactively.

This study analyzes the alignment between (1) the scientific publications generated within a nation-state, and (2) the United Nations resolutions on climate change research and sustainable development guidelines to which that nation-state is a signatory. Starting with a characterization of Chile’s modern scientific journal output using extensive scientometrics databases, this work contrasts the resulting Chilean analysis with United Nations (UN) resolutions generated at the fifth UN Environment Assembly held in Nairobi in March of 2022. Chile is an interesting choice because the most recent political election installed an environmentally progressive president who has described his administration as the “first ecological government in the history of Chile”. Chile also held a constitutional referendum shortly after the presidential election that would have replaced the existing constitution from 1980 with a more progressive constitution designed to shift the country towards expanded social and environmental rights. The study covers different dimensions and scales, from the interaction of authors, institutions, and disciplines, to the current conditions regarding authors’ gender and the co-author inertia existing in every niche of scientific publication in Chile. The results and recommendations presented in this paper are intended to assist in developing policies for improved scientific–technical knowledge management at the national level.

There is a growing concern about the effects that the relationship between the activity of society in the physical world and in the digital world could have. In this study, we address this question in a context of social crisis. Our quantitative and qualitative analysis of the data associated with the critical process suggests a deep and nontrivial relationship between both worlds. Perhaps the most important result refers to the leading role of language, its meaning, and symbolism in the development of social transformation processes linked to the complexity of the social system and its adaptive nature.

Political polarization can emerge on electoral campaigns where the population faces a dichotomous decision, with only two voting alternatives. In this paper, we analyze the Twitter conversation around the second round of the 2017 Chilean elections, where voters had to choose between the final two candidates. First, we have estimated the opinions of Twitter users obtaining a distribution of opinions for each day. Next, we have measured the resulting political polarization from the cited opinion distributions and track its evolution during a full week that includes the voting day. We found the conversation to be highly polarized, reaching its maximum during the previous day to the election and significantly decreasing the day after due to the presence of new users who only participated during that day.

In this work we develop a model that detects potential development of scientific disciplines in universities. The model is based on the position of superimposed disciplines developed by universities on a historical knowledge network by complement. We have observed that in our case study (WoS publications of five Chilean universities during the period 2008-2015), the model achieved up to 78 % on the prognostic of total scientific disciplines that a university has developed, so it is offered as a valid tool to guide universities to the development of another areas of knowledge.

Social systems are always exposed to critical processes in which their organization, or part of it, is questioned by the society that demands solutions through different critical saliences. The traditional approach to such social crises has mainly focused on their anticipation and management, implying that the focus is on trying to deal with crises once they occur, rather than delving in their essential characteristics that seemingly depend on the adaptive nature of the system and the increase in its internal complexity. To address this issue, we propose a dual approach that utilizes both qualitative (documentary analysis) and quantitative methods (online social network analysis) in order to delve into the relationship between the complexity of the social system, its adaptation, and critical episodes. Our analysis shows how an explosive economic growth affects a social system, increasing its complexity. This complexity produces different demands from the system itself. These demands manifest signatures of complexity such as a heterogeneous and rich social structure, which emerges during moments when the society acts strongly.

In this work we analyzed the relationships between powerful politicians and businessmen of Chile in order to study the phenomenon of social power. We developed our study according to Complex Network Theory but also using traditional sociological theories of Power and Elites. Our analyses suggest that the studied network displays common properties of Complex Networks, such as scaling in connectivity distribution, properties of small-world networks, and modular structure, among others. We also observed that social power (a proposed metric is presented in this work) is also distributed inhomogeneously. However, the most interesting observation is that this inhomogeneous power and connectivity distribution, among other observed properties, may be the result of a dynamic and unregulated process of network growth in which powerful people tend to link to similar others. The compatibility between people, increasingly selective as the network grows, could generate the presence of extremely powerful people, but also a constant inequality of power where the difference between the most powerful is the same as among the least powerful. Our results are also in accordance with sociological theories.

<![CDATA[El objetivo de este trabajo es proponer un algoritmo para la clasificación automática de textos, como una alternativa a los tradicionalmente utilizados en esta tarea. El clasificador propuesto considera la dependencia entre las variables predictoras (palabras o términos), algo que los clasificadores de texto comúnmente utilizados no hacen. La dependencia entre estas variables queda plasmada en forma de enlaces en grafos de palabras co-ocurrentes, objetos utilizados para entrenar el clasificador y además estimar la categoría de un texto desconocido. Los resultados obtenidos al clasificar automáticamente el sentido positivo, negativo o neutral de más de 1.000 mensajes de Twitter escritos en español, en distintos contextos (temas), muestran que el algoritmo, además de ser una propuesta novedosa para la clasificación automática de textos, tiene un desempeño, al menos, similar al de otros tradicionalmente utilizados en este tipo de problemas, como las Máquinas de Soporte Vectorial o algoritmos de estadística Bayesiana.]]>