Australasian Marketing Journal

The study of complex systems through agent based modelling present opportunities for marketing researchers to develop time and space explanations of interactions that occur in the marketplace and determine emergent phenomena, such as the adoption of new technology or successful business networks. The use of simulations and the ideas of complex systems though may appear baffling to many and the acceptance of simulations, especially agent based models has a long way to go given concerns about the validity and realism of many models. In this special issue we aim to present a number of papers which show a wide range of applications of agent based models to study business environments and consumer behaviour. There are also theoretical and methodological papers dealing with this new research paradigm. The validation of simulation models both by competing programs and with real world data is discussed in this special issue.

We discuss the use of Agent-based Modelling for the development and testing of theories about emergent social phenomena in marketing and the social sciences in general. We address both theoretical aspects about the types of phenomena that are suitably addressed with this approach and practical guidelines to help plan and structure the development of a theory about the causes of such a phenomenon in conjunction with a matching ABM. We argue that research about complex social phenomena is still largely fundamental research and therefore an iterative and cyclical development process of both theory and model is to be expected. To better anticipate and manage this process, we provide theoretical and practical guidelines. These may help to identify and structure the domain of candidate explanations for a social phenomenon, and furthermore assist the process of model implementation and subsequent development. The main goal of this paper was to make research on complex social systems more accessible and help anticipate and structure the research process.

Given an international business network with the same focal resource, the same source and markets, but exhibiting two different inter-related sub-networks with different internal organization, we study how these network forms affects interactions. The purpose is to compare and explain differences between the two network forms and the effects this have on dyadic international relationship development using a qualitative experimental methodology involving computerized simulations. We simulate various changes in quality variation of the focal resource as well as changing demand preferences of buyers to investigate the impact on relationship strength. From this we develop three scenarios.

Complex systems generate complex information structures. Understanding and managing the behavior of systems, including business systems, requires the study of these complex structures to gain greater understanding of the processes and mechanisms at play in their generation, self-replication and evolution. However, the study of the information generated by such complex structures requires going beyond traditional analytical approaches, i.e. many/most of the existing statistical methods. This paper considers the nature of complex systems in information terms and discusses the issues associated with traditional measurement and summary of their information. The paper then introduces new approaches to conceptualizing and measuring data generated by complex social systems that address these issues by mapping the interaction(s) of the systems’ agents through space–time.

Community-based social marketing (CBSM) involves members of the community as active participants in the marketing campaign for a social good. However behaviour of community members in CBSM is not well simulated using the standard tools available to marketers. We show how agent-based models (ABMs) can be used to simulate the behaviour of community members at the individual level to determine how sensitive the outcome of a CBSM campaign is to assumptions around the effectiveness of marketing within the community. We develop an ABM for wetlands managers to use to simulate the outcome of a marketing plan for promotion of environmental tourism in a wetlands area. The wetlands managers must trade off the costs of marketing and the damage done by the tourism activities with the value of ecotourism for the wetland. We find evidence from the simulations that wetlands’ ecological health is sensitive to the design of the social marketing campaign.

This paper seeks to understand how we might identify the “underlying logics” and “deeper structures” that bring about change in phenomena. We argue that this represents a move from a classical perspective focusing on discrete exchange, and that this requires a processual or relational approach to understanding in contrast to a substantialist or variables-based approach. One way of advancing our understanding of the emergence of change is to consider the site of interaction. That is the interactional field where actors act and interact with other actors and entities as well as the broader environment; where resources are exchanged, imported or exported; where change is instigated and transferred across time and space. We suggest interactional fields are the sites of plasticity where change actually takes place. To understand the causal structure and processes taking place in an interactional field we draw on the concept of natural and social kinds. We discuss how interactional fields are located in time and space, which influence and are influenced by the trajectories of change and development. While we believe this applies to change in general we apply our thinking to organizational change.

The emergence and deterioration of trust between agents is a critical feature of many open distributed systems. This paper reports on a simulation of one aspect of relationships: the behaviour of peer groups when confronted with lying. It presents an abstract model of shared cognitive space, using NetLogo, where agents (1) determine a perception of trust towards other agents, (2) interrogate nearby agents, and (3) reassess their perceptions of trust based on judgements of others. (4) Some agents are discovered to be liars, (5) causing agents to move away from the liar in their cognitive space. Group cohesion is tested by varying (i) decay of agents’ memory, (ii) penalty for lying, (iii) proportion of liars, (iv) probability of discovery, and (v) distance an agents moves away from a liar in cognitive space. We find that penalty for lying has little effect on group cohesion, but the other factors can cause significant disruption.

It is a common phenomenon that at any gathering, people cluster into small and multiple groups to: chat, exchange ideas, establish relations, and explore collaborative opportunities either within their field of work or even in newer frontiers. Certain relationships remain strong and may eventually lead to fruitful collaborations while others may be short lived. Depiction and/or modelling of such an emergent social networking behaviours are inherently complex. With this motivation, in the context of an academic conference, this research focuses on the development of ‘Networking of Scholars at an Academic Conference (NASC) Model’ using Agent Based Modelling and Simulation (ABMS) technique. The paper describes the model and its implementation in NetLogo. NetLogo is powerful programming environment that facilitates the generation of scenarios and thus helps visualisation of emergent network of relationships among scholars.

This paper shows how sensitivity analysis can be used as part of model verification and validation Sensitivity analysis provides insights on where future data validation processes should focus and which inputs may be considered for model reduction. We compared two approaches, one using a systematic variation of parameter values, another using an optimised algorithm to make more efficient the search of their space. Analysis was conducted on an agent-based model that explores the emergence of innovation within business networks, where successful innovation is considered an increase in knowledge and financial resources within the network. The two sensitivity analysis approaches differed both on their time efficiency and on the type of information provided. While the systematic individual sensitivity analysis assisted us in identifying inputs with substantial impact upon the results and suggest solutions for model simplification, the optimised search provided insights on the network resources likely to achieve higher levels of innovation. Genetic algorithms found parameter values that produced different results in the agent-based model.

We present a conceptual model where agents are prompted to adopt a new technology through a two-step process: information from neighbours prompts an upgrade, and the option purchased may be influenced by the one demonstrated by the neighbour. In a network world with two options available we systematically manipulate (1) the initial number of neighbours with white compared to black, (2) rate of naturally-occurring upgrade, (3) chance of upgrade prompted by a neighbour using white relative to black, and (4) the relative chance of choosing white instead of black having decided to upgrade. Not surprisingly, adoption speed is influenced by starting users, natural upgrade, and relative upgrade chance. Market share, on the other hand, is influenced only by the relative chance of choosing white over black, with no influence at all from the other predictors. We find that this result applies regardless of the type or complexity of network.