As competitive environments are becoming turbulent, management science is showing increasing interest in the concept of emergence. This concept is closely related to the new science of complexity and its agent-based approach. While this agent-based approach of complexity science is on its way of becoming a new paradigm in management science, the system dynamics approach is suffering from a lack of management attention and impact, although both are being applied to similar problems with similar outcomes. In previous research, scholars have compared system dynamics modeling and agent-based modeling. This paper steps back from the modeling aspect and looks at the requisite conditions, as identified by complexity science, that make emergent phenomena happen. Complex adaptive systems are agent-based systems capable of balancing emergent efficiency and innovation without central control. In this paper we give a comprehensive overview of mechanisms and principles of complex adaptive systems that are prerequisites of emergent phenomena. We propose a generic framework of complex adaptive systems for use in management science. This framework then serves to assess exemplary literature on applications of complexity science to firms and to discuss research implications for both agent-based and system dynamics approaches to management problems.
Complexity of organization and resulting dynamic behavior are prominent attributes of the U.S. health care system. These characteristics often compel analysts to deconstruct problems and take a piece-by-piece approach. However, such a piecewise approach may miss subtle and powerful interactions within the larger health care system structure. System dynamics methodology can be applied to identify and resolve issues arising in complex social systems such as health care. In a commercial managed care organization, a comprehensive system dynamics simulation model of the U.S. health care system helps users to make sense of systemic behavior and forecast key trends.
To study the long-term usefulness of genetically-modified agriculture via herbicide-tolerant crops, a simulation model is built by focusing on the fundamental environmental feedback mechanisms. The most critical mechanism is the evolution of resistance in weeds via natural selection. Agricultural sustainability is investigated under different policies and scenarios, in comparison with conventional crops under two herbicide strategies. In the first strategy, herbicide amount is a function of weed density; in the second it is constant. It is found that superweed emergence increases the rate of resistance evolution in weeds. Under the constant herbicide strategy, GM crop is more effective than the conventional crop. However, this strategy results in a higher rate of resistance development and more herbicide usage than the first strategy. In terms of long term cumulative yield losses, rate of resistance development and herbicide usage, the best policy is discovered to be planting conventional crops under variable herbicide strategy.
Models for numerical simulations should be described in a coherent style. They are expected to have consistencies at the causal dependency level. However, System Dynamics causal loop diagrams can have inconsistencies. This diagram styles arrows, concerning flow and stock relationships, can have the opposite direction of stock flow diagrams which can numerically simulate models. These inconsistencies can cause inappropriate qualitative simulations so that it is sometimes recommended to use stock flow diagrams instead of causal loop diagrams even for qualitative simulations. However, causal loop diagrams have merits in their use. Causal loop diagrams are intuitively easy to draw and read. If causal loop diagrams are given information about each variables dynamic property, they can be changed to stock flow diagrams and simulation models can be generated. This paper suggests how to use causal loop diagrams as a starting point in numerical simulation research.
Beer Game is one of famous business games. Indeed, it is played or learned in many universities, graduate schools, and company trainings. This games original version requires a game board and some pieces which are substitutes for real beer boxes or bills in the original version. After that, computer versions have been already produced. The original version provides the learning environment with reality, and the computer version offers the self-learning environment. Nevertheless, facilitators still want additional improvements; this game should be easily installed, run quickly, provide a real competition environment and have no influence on installed computer. Thus, users expect Nomadic Beer Game environment, because this game is used in a short period or part of one seminar. Existing software can partly meet such expectations. However, there is no software implemented whole these functions. This research project analysed Beer Game users expectations and composed software meeting their needs is constructed.
Low retention of valuable employees and difficulties in finding qualified candidates for recruitment are two issues managers face in Romania, but are a growing concern around the world (Deloitte, 2004; Holton & Naquin, 2004). High turnover of specialists disrupts organizational continuity (Lum, et al,1998) and the current policies dont seem to have the expected results, according to the field study of the author. We suspect that the cause of inefficient policies lies in a misperception of dynamics, ignorance of feedback loops and of intangible stocks, like the job satisfaction of employees. Although well documented in psychology literature (Lichenstein, 1998),the influence of job satisfaction on turnover seems to be ignored by the policy makers. We test policies that account for the determinants of job satisfaction and show that the outcome of such policies is better than the current ones. Implications for HR policy design and directions for future research are indicated.
This article uses the framework proposed by Rouwette et al. (2002) that has been tested by Stave (2005) to report group model building intervention of a project to develop vehicle emission reduction strategy in Jakarta, Indonesia. The project is among the first attempt that was conducted in the developing country to solve the public issue. Many rapidly developing countries exhibit a faster vehicle emission grows over time compare to those of developed world at comparable income per capita (Marcotulio et al., 2005:125). Consequently, the need to solve the problem under the economic restriction is pressing. The case supports the findings of Rouwette et al. (2002) regarding a strong connection between group model building and group learning about the problem. The group has not reached a consensus, as the participants who attended the final meeting were not broad enough. Nevertheless, the findings demonstrate a potential of the group model building intervention to build a consensus among stakeholders. The approach in setting up the workgroup needs a modification to enhance the stakeholders participation throughout the meetings.
To reduce costs (by ca. 30%), increase production (by ca. 10%) and extend the life time (by ca. 5 years) of North Sea wells the Norwegian oil & gas industry is developing an infrastructure of "integrated operations" i.e. eOperations from control centers with reduced personnel on offshore platforms. New technology, new work processes and new knowledge are needed. Increased reliance on information technology introduces risks, with components of threat, vulnerability, and impact depending on how the transition to eOperations is managed. Simulation models show that the risk behavior of the system depends sensitively on how resources for work process development and knowledge ac-quisition are deployed. Understanding such dynamics facilitates decision-making to minimize security (and safety) risks.
This paper develops a dynamic, behavioral model with explicit spatial structure to explore the co-evolutionary dynamics between infrastructure supply and vehicle demand. These "chicken-egg" dynamics are fundamental to the emergence of a self-sustaining alternative fuel vehicle market, but not well understood. The paper explores in depth the dynamics resulting from local demand-supply interactions with strategically locating fuel-station entrants. We examine dynamics under heterogeneous socio-economic/demographic conditions. The research reveals the formation of urban adoption clusters as an important mechanism for early market formation. However, while locally speeding diffusion, these same micro-mechanisms can obstruct the emergence of a large sustaining market. Several other feedbacks that significantly influence dynamics, from both supply and demand behaviors are discussed. This can find applications in developing targeted entrance strategies for alternative fuel in transportation. The roles of other powerful positive feedbacks arising from scale and scope economies, R&D, learning by doing, driver experience, and word of mouth are discussed.
The automotive industry is on the verge of a technological disruption as more and different alternative fuel vehicles are expected to enter the market soon. Several questions come up for the many who contemplate on the next standard. However, industry evolution theories are not unified under what conditions entrant technologies can be successful. Technology transitions are dynamically complex, because of the interdependency of delays in return from R&D efforts, the intertemporal character of learning, investment commitment, technology spillovers, scale economies and other barriers to entry, replacement dynamics, consumer choice behavior, uncertainty in technology valuation, heterogeneity in technologies, and various sources of organizational inertia.
This paper introduces a semi-durable goods product life cycle model, with explicit and endogenous technology heterogeneity, product innovation, learning-by-doing investment decisions, and spillovers between the technologies. While motivated by, and its dynamics are discussed in relation to the automobile industry, the model is general in the sense that it can be calibrated for different industries with specific market-, technology-, and organizational characteristics. We explore its dynamics and discuss implications.