Emergence in stigmergic and complex adaptive systems: A formal discrete event systems perspective

Path dependence, stigmergy, and memetic reification in the formation of the 27 Club myth

The "27 Club" refers to the widespread legend that notable people, particularly musicians, are unusually likely to die at age 27. A 2011 inquiry in The BMJ showed this is not the case, dismissing the 27 Club as a myth. We expand on this discourse by demonstrating that although the existence of the phenomenon cannot be empirically validated, it is real in its consequences. Using Wikipedia data, we show that while age 27 does not hold greater risk of mortality for notable persons, those who died at 27 are as a group exceptionally notable compared to those who died at other young ages. The 27 Club legend originated from a statistically improbable event circa 1970, wherein four superstar musicians died within the span of 2 y all at age 27. This coincidence captured the public imagination such that our fascination with the 27 Club brought itself into being, producing greater interest in those who died at age 27 than would have been otherwise. This demonstrates path dependence in cultural evolution, whereby an effectively random event evolves into a narrative that shapes otherwise unrelated events and thus the way we make and interpret history.

Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep Uncertainty

Within the modeling and simulation community, simulation-based optimization has often been successfully used to improve productivity and business processes. However, the increased importance of using simulation to better understand complex adaptive systems and address operations research questions characterized by deep uncertainty, such as the need for policy support within socio-technical systems, leads to the necessity to revisit the way simulation can be applied in this new area. Similar observations can be made for complex adaptive systems that constantly change their behavior, which is reflected in a continually changing solution space. Deep uncertainty describes problems with inadequate or incomplete information about the system and the outcomes of interest. Complex adaptive systems under deep uncertainty must integrate the search for robust solutions by conducting exploratory modeling and analysis. This article visits both domains, shows what the new challenges are, and provides a framework to apply methods from operational research and complexity science to address them. With such extensions, simulation-based approaches will be able to support these new areas as well, although optimal solutions may no longer be obtainable. Instead, robust and sufficient solutions will become the objective of optimization processes.

Spider webs inspiring soft robotics

In soft robotics, bio-inspiration ranges from hard- to software. Orb web spiders provide excellent examples for both. Adapted sensors on their legs may use morphological computing to fine-tune feedback loops that supervise the handling and accurate placement of silk threads. The spider's webs embody the decision rules of a complex behaviour that relies on navigation and piloting laid down in silk by behaviour charting inherited rules. Analytical studies of real spiders allow the modelling of path-finding construction rules optimized in evolutionary algorithms. We propose that deconstructing spiders and unravelling webs may lead to adaptable robots able to invent and construct complex novel structures using relatively simple rules of thumb.

MBSE with/out Simulation: State of the Art and Way Forward

The limitations of model-based support for engineering complex systems include limited capability to develop multifaceted models as well as their analysis with robust reliable simulation engines. Lack of such Modeling and Simulation (M&S) infrastructure leads to knowledge gaps in engineering such complex systems and these gaps appear as epistemological emergent behaviors. In response, an initiative is underway to bring Model-Based Systems Engineering (MBSE) closer together with model-based simulation developments. M&S represents a core capability and is needed to address today’s complex, adaptive, systems of systems engineering challenges. This paper considers the problems raised by MBSE taken as a modeling activity without the support of full strength integrated simulation capability and the potential for, and possible forms of, closer integration between the two streams. An example of a system engineering application, an unmanned vehicle fleet providing emergency ambulance service, is examined as an application of the kind of multifaceted M&S methodology required to effectively deal with such systems.

Explaining the emergence of team agility: a complex adaptive systems perspective

Purpose

Agile software development helps software producing organizations to respond to manifold challenges. While prior research focused on agility as a project or process phenomenon, the authors suggest that agility is an emergent phenomenon on the team level. The paper aims to discuss this issue.

Design/methodology/approach

Using the theory of complex adaptive systems (CASs), the study captures the multiple influencing levels of software development teams (SDTs) and their interplay with self-organization and emergence. The authors investigate three agile SDTs in different contextual environments that participate with four or more different roles each.

Findings

The results suggest self-organization as a central process when understanding team agility. While contextual factors often provide restriction on self-organization, they can help the team to enhance its autonomy.

Research limitations/implications

The theoretical contributions result from the development and test of theory grounded propositions and the investigation of mature agile development teams.

Practical implications

The findings help practitioners to improve the cost-effectiveness ratio of their team’s operations.

Originality/value

The study provides empirical evidence for the emergence of team agility in agile SDTs. Using the lens of CAS, the study suggests the importance of the team’s autonomy.