Soft Skills Centrality in Graduate Studies Offerings

María del Pilar García-Chitiva, Juan C. Correa

Is it possible to measure how critical soft skills like leadership or teamwork are from the viewpoint of graduate studies offerings? This paper provides a conceptual and methodological framework that introduces the concept of a bipartite network as a practical way to estimate the importance of soft skills as socio-emotional abilities trained in graduate studies. We examined 230 graduate programs offered by 49 higher education institutions in Colombia to estimate the empirical importance of soft skills from the viewpoint of graduate studies offerings. The results show that: a) graduate programs in Colombia share 31 soft skills in their intended learning outcomes; b) the centrality of these skills varies as a function of the graduate program, although this variation was not statistically significant; and c) while most central soft skills tend to be those related to creativity (i.e., creation or generation of ideas or projects), leadership (to lead or teamwork), and analytical orientation (e.g., evaluating situations and solving problems), less central were those related to empathy (i.e., understanding others and acknowledgment of others), ethical thinking, and critical thinking, posing the question if too much emphasis on most visible skills might imply an unbalance in the opportunities to enhancing other soft skills such as ethical thinking.

Read the full article at: arxiv.org

Evolving higher-order synergies reveals a trade-off between stability and information integration capacity in complex systems

Thomas F. Varley, Joshua Bongard

There has recently been an explosion of interest in how “higher-order” structures emerge in complex systems. This “emergent” organization has been found in a variety of natural and artificial systems, although at present the field lacks a unified understanding of what the consequences of higher-order synergies and redundancies are for systems. Typical research treat the presence (or absence) of synergistic information as a dependent variable and report changes in the level of synergy in response to some change in the system. Here, we attempt to flip the script: rather than treating higher-order information as a dependent variable, we use evolutionary optimization to evolve boolean networks with significant higher-order redundancies, synergies, or statistical complexity. We then analyse these evolved populations of networks using established tools for characterizing discrete dynamics: the number of attractors, average transient length, and Derrida coefficient. We also assess the capacity of the systems to integrate information. We find that high-synergy systems are unstable and chaotic, but with a high capacity to integrate information. In contrast, evolved redundant systems are extremely stable, but have negligible capacity to integrate information. Finally, the complex systems that balance integration and segregation (known as Tononi-Sporns-Edelman complexity) show features of both chaosticity and stability, with a greater capacity to integrate information than the redundant systems while being more stable than the random and synergistic systems. We conclude that there may be a fundamental trade-off between the robustness of a systems dynamics and its capacity to integrate information (which inherently requires flexibility and sensitivity), and that certain kinds of complexity naturally balance this trade-off.

Read the full article at: arxiv.org

WOSC 19th Congress 2024

September 11-13, 2024 in Lady Margaret Hall, Oxford, UK

Shaping collaborative ecosystems for tomorrow

The complexity of interactions and relationships in our world have consistently surpassed our ability to fully comprehend and govern. The presence of intelligent tools, both in the digital and physical realms, is progressively enhancing our capacities to act on personal, organizational, national, and international levels, leading to both intended and unintended consequences. Collectively, these changes are reshaping our primary habitat—the planet Earth—at a speed and scale that necessitate earnest consideration. In the midst of uncertainty, the development and utilization of these new capabilities would greatly benefit from CyberSystemic approaches and methods of learning. This advancement is crucial for fostering a sustainable understanding and taking actions to avert major threats to our civilization.

More at: wosc.world

Defining Complex Adaptive Systems: An Algorithmic Approach

Ahmad, M.A.; Baryannis, G.; Hill, R

Systems 2024, 12(2), 45

Despite a profusion of literature on complex adaptive system (CAS) definitions, it is still challenging to definitely answer whether a given system is or is not a CAS. The challenge generally lies in deciding where the boundaries lie between a complex system (CS) and a CAS. In this work, we propose a novel definition for CASs in the form of a concise, robust, and scientific algorithmic framework. The definition allows a two-stage evaluation of a system to first determine whether it meets complexity-related attributes before exploring a series of attributes related to adaptivity, including autonomy, memory, self-organisation, and emergence. We demonstrate the appropriateness of the definition by applying it to two case studies in the medical and supply chain domains. We envision that the proposed algorithmic approach can provide an efficient auditing tool to determine whether a system is a CAS, also providing insights for the relevant communities to optimise their processes and organisational structures.

Read the full article at: www.mdpi.com