Co-evolutionary Approach Research Articles

MNEs as institutional entrepreneurs: A dynamic model of the co-evolutionary process

Co-evolutionary theory suggests that firms and their environments interactively influence each other over time due to the interplay between them. However, international business (IB) literature has paid little attention to the study of the co-evolutionary process. Therefore, new research that delves into the process in order to identify how the reciprocal influences between institutions and multinational enterprises (MNEs) take place, and that identifies key variables that determine the extent to which MNEs will affect the environment, is necessary. The current study addresses this call and examines the interplay between institutions and MNEs in order to explain such a coevolution. The paper provides a cohesive theoretical model for the co-evolutionary approach in the IB literature. Some suggestions for lines of inquiry and of methodological challenges for future research in the IB field are provided.

A coevolutionary approach to many objective optimization based on a novel ranking method

Ranking solutions of the population in an evolutionary algorithm that solves a many objective optimization problem is a challenging task which has been vastly studied in recent years. Loss in the hypervolume of the population when a solution is omitted could be a good measure for ranking solutions but calculating this value for high dimensional problems is not tractable. In the selection operator of evolutionary algorithms, the actual hypervolume values are not important, with only a relative knowledge of these values we can select best solutions. In this paper we have proposed a novel method for approximating the ranking induced by the hypervolume indicator. This method is compared to the similar methods and its efficiency and performance is proved via proper experiments. The method is tested using benchmark test problems from the Walking Fish Group problem family which are scalable both in number of variables and objectives.

Fitness function shaping in multiagent cooperative coevolutionary algorithms

Coevolution is a promising approach to evolve teams of agents which must cooperate to achieve some system objective. However, in many coevolutionary approaches, credit assignment is often subjective and context dependent, as the fitness of an individual agent strongly depends on the actions of the agents with which it collaborates. In order to alleviate this problem, we introduce a cooperative coevolutionary algorithm which biases the evolutionary search as well as shapes agent fitness functions to promote behavior that benefits the system-level performance. More specifically, we bias the search using a hall of fame approximation of optimal collaborators, and shape the agent fitness using the difference evaluation function. Our results show that shaping agent fitness with the difference evaluation improves system performance by up to 50 %, and adding an additional fitness bias improves performance by up to 75 % in our experiments. Finally, an analysis of system performance as a function of computational cost demonstrates that this algorithm makes extremely efficient use of computational resources, having a higher performance as a function of computational cost than any other algorithm tested.

Adaptive selection and coevolution at the proteins of the Polycomb repressive complexes in Drosophila.

Polycomb group (PcG) proteins are important epigenetic regulatory proteins that modulate the chromatin state through posttranslational histone modifications. These interacting proteins form multimeric complexes that repress gene expression. Thus, PcG proteins are expected to evolve coordinately, which might be reflected in their phylogenetic trees by concordant episodes of positive selection and by a correlation in evolutionary rates. In order to detect these signals of coevolution, the molecular evolution of 17 genes encoding the subunits of five Polycomb repressive complexes has been analyzed in the Drosophila genus. The observed distribution of divergence differs substantially among and along proteins. Indeed, CAF1 is uniformly conserved, whereas only the established protein domains are conserved in other proteins, such as PHO, PHOL, PSC, PH-P and ASX. Moreover, regions with a low divergence not yet described as protein domains are present, for instance, in SFMBT and SU(Z)12. Maximum likelihood methods indicate an acceleration in the nonsynonymous substitution rate at the lineage ancestral to the obscura group species in most genes encoding subunits of the Pcl-PRC2 complex and in genes Sfmbt, Psc and Kdm2. These methods also allow inferring the action of positive selection in this lineage at genes E(z) and Sfmbt. Finally, the protein interaction network predicted from the complete proteomes of 12 Drosophila species using a coevolutionary approach shows two tight PcG clusters. These clusters include well-established binary interactions among PcG proteins as well as new putative interactions.

Experiential Lock-In: Characterizing Avoidable Maladaptation in Infrastructure Systems

Facing the combined challenges of environmental, social, and technological change, long-lived infrastructure systems run the risk of getting locked into unsustainable, maladapted pathways. This is particularly challenging in the context of climate change, given projected climate impacts are characterized by high degrees of uncertainty (Hallegatte 2009). Lock-in is a concept developed by economic historians to describe how economies get tied into using inefficient technologies, and it is linked to the concept of path dependence (Arthur 1983; David 1985), which refers to the fact that infrastructure systems follow specific trajectories that are difficult and costly to change. As shown in Arthur (1989), these trajectories depend on historical circumstances, timing, and strategy as much as on optimality. In the 1990s, some investigations highlighted the need to approach the analysis of technological changes through coevolutionary approaches that recognize the technological systems influences and are influenced by the social, economic, and cultural setting in which they develop (Rip and Kemp 1998). Liebowitz and Margolis (1994) argued that the role of some elements of the system, such as network externalities, remains contested. Of particular interest is the extent to which favoring incumbent infrastructure systems limits the development capacity of socioeconomic groups such as communities, industries, or countries. While exploring the whole phase space of possible fundamental influences is impractical, the authors argue that it is still possible to avoid some lock-in by effectively utilizing existing anticipatory capacity. The paper elaborates on three ideas, firmly rooted in the scholarly literature and recent studies, which characterize one type of avoidable lock-in: (1) the observed dominance of experiential versus analytical anticipatory capacity of communities, industries, and countries in the governance of sociotechnical systems; (2) the existence of formal approaches to quantify the limits to adaptation in such systems; and (3) limitations of the impact and capacity approach to adaptation. The elements of an avoidable lock-in are then summarized and illustrated by an example. Finally, some conclusions are given on the implications of this type of avoidable lock-in and how it might increasingly affect policy decisions that have long-term implications, such as those related to long-lasting infrastructure systems and spatial planning.

Multi-objective optimal design of hybrid renewable energy systems using preference-inspired coevolutionary approach

As the increasing energy demand and rapid depletion of conventional fossil fuel resources, renewable energy has caused great attention of the public. The main drawback of the renewable resources is their unpredictable nature. A hybrid renewable energy system (HRES) that integrates different resources in proper combination is a promising solution to overcome this challenge. In this context, the preference-inspired coevolutionary algorithm (PICEA) has been applied for the first time to the design of multi-objective hybrid renewable energy system. We propose an enhanced fitness assignment method to improve the preference-inspired coevolutionary algorithm using goal vectors (PICEA-g) in the optimization process minimizing, simultaneously, the annualized cost of system (ACS), the loss of power supply probability (LPSP) and the fuel emissions. As an example of application, a stand-alone hybrid system including PV panels, wind turbines, batteries and diesel generators has been designed to find the best combination of components, achieving a set of non-dominated solutions from which the decision maker can select a most adequate one.

Mobile transitions: Exploring synergies for urban sustainability research

Urban sustainability approaches focusing on a wide range of topics such as infrastructure and mobility, green construction and neighbourhood planning, or urban nature and green amenities have attracted scholarly interest for over three decades. Recent debates on the role of cities in climate change mitigation have triggered new attempts to conceptually and methodologically grasp the cross-sectorial and cross-level interplay of enrolled actors. Within these debates, urban and economic geographers have increasingly adopted co-evolutionary approaches such as the social studies of technology (SST or ‘transition studies’). Their plea for more spatial sensitivity of the transition approach has led to promising proposals to adapt geographic perspectives to case studies on urban sustainability. This paper advocates engagement with recent work in urban studies, specifically policy mobility, to explore conceptual and methodological synergies. It emphasises four strengths of an integrated approach: (1) a broadened understanding of innovations that emphasises not only processes of knowledge generation but also of knowledge transfer through (2) processes of learning, adaptation and mutation, (3) a relational understanding of the origin and dissemination of innovations focused on the complex nature of cities and (4) the importance of individual actors as agents of change and analytical scale that highlights social processes of innovation. The notion of urban assemblages further allows the operationalisation of both the relational embeddedness of local policies as well as their cross-sectoral actor constellations.

Support your future today: enhancing sustainable transitions by experimenting at academic conferences

Major societal changes which challenge societal functions and actors' activities are needed to enhance sustainable development. Thus sustainable transitions research emphasizes co-evolutionary approaches involving a multitude of actors including the business sector, the government, and academia. Academic research can catalyse sustainable transitions by critically analyse current societal trends to develop and disseminate new knowledge. At research conferences, researchers and practitioners meet to network and discuss recent research findings providing arenas for testing and evaluating ideas to enhance sustainable transitions. This however requires some modifications of the standard design of a research conference.Here we report learning outcomes from experimenting at the 18th international Greening of Industry Network conference during 21–24 October 2012 in Linköping, Sweden. The conference was a combination of a traditional conference structure with different interactive elements such as sustainability jam-sessions to discuss future challenges of six companies and clusters of companies at their site. The intention of doing so was to enhance learning outcomes both for visiting conference delegates and among actors in the host region. This was perceived by the participants as an innovative approach fostering both problem solving and creation of new ideas. Four out of the six companies continued dialogues about sustainable production fields or bio-refineries with Linköping University.In addition we introduce and summarize research findings presented at the conference which were further developed into research articles. The essence of these articles covers sustainable industry management; cleaner production; industrial ecology; cooperation between industry, governments and academics; dissemination of concepts and technologies; methods and tools for modelling and measuring of industrial symbiosis, CO2 performance and eco-efficiency.

Darwinism, organizational evolution and survival: key challenges for future research

How do social organizations evolve? How do they adapt to environmental pressures? What resources and capabilities determine their survival within dynamic competition? Charles Darwin’s seminal work The Origin of Species (1859) has provided a significant impact on the development of the management and organization theory literatures on organizational evolution. This article introduces the JMG Special Issue focused on Darwinism, organizational evolution and survival. We discuss key themes in the organizational evolution research that have emerged in recent years. These include the increasing adoption of the co-evolutionary approach, with a particular focus on the definition of appropriate units of analysis, such as routines, and related challenges associated with exploring the relationship between co-evolution, re-use of knowledge, adaptation, and exaptation processes. We then introduce the three articles that we have finally accepted in this Special Issue after an extensive, multi-round, triple blind-review process. We briefly outline how each of these articles contributes to understanding among scholars, practitioners and policy makers of the continuous evolutionary processes within and among social organizations and systems.

Fuzzy tales for hard blueprints: the selective coproduction of the Spatial Policy Plan for Flanders, Belgium

Planners become increasingly aware that they operate in a fuzzy, fragmented, volatile, and unpredictable world. For a couple of decennia they have tried to find new answers in a communicative turn, in collaborative or in coproductive planning. However, because of the ongoing fragmentations and complexities, such attempts have shown to be so time-consuming, vague, or framed by the regular suspects of planning that they hardly meet today's major challenges. We will demonstrate this by means of the planning process for the forthcoming Spatial Policy Plan for Flanders, the successor of the Spatial Structure Plan for Flanders—a strategic plan on which the literature has extensively reported. In 1997 this latter plan was endorsed and accepted by most of the authorities concerned. More than fifteen years later, however, original commitments have eroded, and the ongoing process leading to the adoption of the new plan has failed to build broad support and credibility. Therefore, the new Spatial Policy Plan has turned to the promises of coproductive planning in order to include citizens and interest groups in the planning process. However, we will argue that in Flanders today this borrowed methodology of coproductive planning is insufficiently adapted to the Flemish context and is therefore mainly delivering an aura of optimism about sustainability to ongoing policies, while a variety of spatial developments that are recognized as very substantive or problematic are kept outside the process. To overcome this, we will finally discuss a preliminary design of a more contextualized process model, putting the stress on more concrete planning issues, involving independent stakeholders in strategic alliances, and taking a coevolutionary approach from the start.

Mergers and Telecommunications Infrastructure Deployment

We retrospectively evaluate the impact of the various mergers of the local exchange companies in the United States telecommunications industry between 1988 and 2001 on technology investment levels. We use treatment effects modeling to evaluate major mergers in a sector where technology diffusion is important, and the approach accounts for selection bias and the endogenous nature of mergers. Our framework is based on behavioral theories of the firms and we use co-evolutionary and dynamic capabilities approaches in framing our analyses and explaining our results. We find that across firms and time, the merger events are positively and significantly associated with investments in fiber and digital assets. We stratify the data by size of firm to undertake detailed data analysis because of the presence of firm-specific heterogeneities and find that for the smaller merging firms there is a negative and significant association of mergers with investments in fiber and digital assets while for the larger merging firms this mergers and fiber and digital assets investments association is positive and significant. Our results support the predictions of the behavioral theories of the firm, and particularly the dynamic capabilities perspective. We draw implications for theory and competition policy of our results.

A Cooperative Coevolution Approach to Automate Pattern-based Software Architectural Synthesis

To reuse successful experience in software architecture design, architects use architectural patterns as reusable architectural knowledge for architectural synthesis. However, it has been observed that the resulting architecture does not always conform to the initial architectural patterns employed. Architectural synthesis using architectural patterns is also recognized as a challenging task, especially for novice architects due to lack of experience. In this paper, we propose a cooperative coevolution approach to automate architectural synthesis using architectural patterns. We first analyze several common architectural patterns and the constraints when using them. We then extend existing architectural synthesis activity with patterns based on the results of this analysis. Finally, we map the extended architectural synthesis to a cooperative coevolution model, which can optimize the resulting architectural solutions and avoid the violations to the pattern constraints automatically. We evaluate the proposed approach through a case study: architecture design of a cinema booking system. The results show that the proposed approach can generate architectural solutions which are closer to the expert design.

The distributed co-evolution of an on-board simulator and controller for swarm robot behaviours

We investigate the reality gap, specifically the environmental correspondence of an on-board simulator. We describe a novel distributed co-evolutionary approach to improve the transference of controllers that co-evolve with an on-board simulator. A novelty of our approach is the the potential to improve transference between simulation and reality without an explicit measurement between the two domains. We hypothesise that a variation of on-board simulator environment models across many robots can be competitively exploited by comparison of the real controller fitness of many robots. We hypothesise that the real controller fitness values across many robots can be taken as indicative of the varied fitness in environmental correspondence of on-board simulators, and used to inform the distributed evolution an on-board simulator environment model without explicit measurement of the real environment. Our results demonstrate that our approach creates an adaptive relationship between the on-board simulator environment model, the real world behaviour of the robots, and the state of the real environment. The results indicate that our approach is sensitive to whether the real behavioural performance of the robot is informative on the state real environment.

Untangling self-reinforcing processes in managerial decision making. Co-evolving heuristics?

Purpose– Over the last 30 years, specific investigations into self-reinforcing processes in managerial decision making have been gaining momentum within the mainstream literature of management. However, to date, it is claimed that understanding these processes properly still requires additional research efforts. Thus, the purpose of this conceptual paper is to follow this claim.Design/methodology/approach– The paper tries to inform the conversation about self-reinforcing processes in managerial decision making through adopting lenses drawing from the biological philosophy of organizational change. In particular, the co-evolutionary approach to organizational change, with a focus on CEOs, and/or top management teams (TMT), is adopted.Findings– As its core contribution, the paper proposes that self-reinforcing processes in the CEOs/TMTs’ decision making can occur because of the emergence (and subsequent consolidation) of co-evolving heuristics. On this basis, the paper also prospect potential avenues for future integrations in this field.Originality/value– As the paper concludes, advancing the general understanding of self-reinforcing processes in managerial decision making can represent an important opportunity for the research and practice of management in general, but also for some management sub-domains, such as that of behavioural strategy, in particular.

Tackling group-level traits by starting at the start.

We agree that emergent group-level properties are important; however, we disagree that current approaches, especially culture-gene coevolutionary (CGC) approaches, have neglected them. We explain how CGC helps demystify the tumult of humans' group-level complexity by "starting at the start," and why (a) assuming undifferentiated individuals and (b) focusing on cooperation are actually powerful tools to this end.

Neurohistory in action: hoarding and the human past.

A neurohistorical approach begins with the principle that the human brain is relatively plastic and therefore continuously open to developmental and cultural influences. This does not mean that we should treat the brain as a blank slate. Instead, such influences, as they interact with given brain/body systems, can generate unpredictable forward-acting effects. The phenomenon of compulsive hoarding offers a case study of a historically or culturally situated behavior that can be approached in this way. Hoarding appears to be correlated with cognitive lesions or genetic predispositions. Yet although the behavior is very visible today, there is little evidence for the practice in the human past, suggesting that something has triggered the growing prevalence of the phenomenon. Using the coevolutionary approach intrinsic to environmental history, we can treat the rise of compulsive hoarding as an emergent phenomenon generated by the unpredictable ways in which cognitive and endocrinological systems have interacted with a changing material environment. The results of this inquiry suggest not only why history needs cognitive neuroscience but also why neuroscience needs history.

Socio-technological Co-evolution Approach: An Endeavor by the JST-RISTEX Environment-energy R&D Program

Bearing in mind the co-evolution nature of transition to a sustainable low carbon system, in this paper, we define a new “co-evolution approach” and implemented it as an R&D funding program of JST-RISTEX. Through the examination of R&D project results, we found that our socio-technological co-evolution approach is effective for involving both academia and local stakeholders at the early stage of scenario building (co-design) and motivating them to commit sustainable local actions together (co-production). This approach initiated by our R&D program is a valuable endeavor implemented even ahead of the “Future Earth” international research initiative for sustainability under the International Council for Science (ICSU).

Enhancing the firefly algorithm through a cooperative coevolutionary approach: an empirical study on benchmark optimisation problems

In recent years, the firefly algorithm (FA) has been applied with success to many classes of optimisation problems. However, as is the case for all metaheuristic optimisation algorithms, also with FA can be observed a rapid deterioration of efficiency as the dimensionality of the search space increases. In this paper, we use a cooperative coevolutionary approach for enhancing FA with the aim of making it much more efficient in the case of search spaces with many dimensions. We assess the performance of the cooperative coevolutionary firefly algorithm (CCFA) through a computational study based on some significant benchmark functions with up to 1,000 dimensions. Moreover, we compare the proposed CCFA with two state-of-the-art algorithms for high-dimensional optimisation problems. According to our results, CCFA can lead to significantly improved solutions in comparison to the standard FA. In addition, we show that the CCFA computation time is significantly lower than that of FA.

Nash Equilibrium in a two-settlement electricity market using competitive coevolutionary algorithms

This paper investigates the use of competitive coevolutionary algorithms to calculate suppliers’ optimal strategies in a deregulated electricity market. The two settlement model is used, consisting of a spot and a forward market. Agents can take part in both spot and forward transactions, and act strategically to maximise their profit from both markets. The strategic interactions of market agents are modelled as a non cooperative game. The competitive coevolutionary Algorithm is used to calculate the Nash Equilibrium strategies ensuring the best outcome for each agent. Results demonstrates the effectiveness of the coevolutionary approach to find the optimal strategies in different market situations.

Family business foundations: theoretical and empirical investigation

Abstract The paper examines family business foundations as connectors between the firm and the family from which they originate, adopting a coevolution approach that considers family and business as two subsystems which coevolve in creating a different species, the family business. Foundations are seen as coevolution facilitators supporting the coevolution process, outlining a specific grow path for family firms, and ensuring family business sustainability. An empirical analysis on Italian family business foundations is developed. The Italian context is taken into consideration because family firms are the backbone of the Italian economy, and the largest family foundations are those created by the most important entrepreneurial families. Data bring to light the structural role played by family business foundations in Italy and their involvement in the field of culture and art, research and training, health care, and territorial responsibility. The analysis shows the growth of family business foundations in recent years and their connection with long-lived companies with whom a close connection is maintained, leading to a lasting intergenerational sustainability.