Academic articles

Heterogeneity and network structure in the dynamics of diffusion

When is it better to use agent-based (AB) models, and when should differential equation (DE) models be used? Whereas DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity across individuals and in the network of interactions among them. AB models relax aggregation assumptions, but entail computational and cognitive costs that may limit sensitivity analysis and model scope. Because resources are limited, the costs and benefits of such disaggregation should guide the choice of models for policy analysis. Using contagious disease as an example, we contrast the dynamics of a stochastic AB model with those of the analogous deterministic compartment DE model. We examine the impact of individual heterogeneity and different network topologies, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Obviously, deterministic models yield a single trajectory for each parameter set, while stochastic models yield a distribution of outcomes.

How to build a combined agent based / system dynamics model in AnyLogic

  AnyLogic allows you to build a simulation model using multiple methods: System Dynamics, Agent Based and Discrete Event (Process‐centric) modeling. Moreover, you can combine different methods in one model: put agents into an environment whose dynamics is defined in SD style, use process diagrams or SD to define internals of agents, etc, etc. Any kind of mixed architecture is possible due to flexible object‐oriented AnyLogic modeling language. The choice of model architecture (how to partition...

Understanding retail productivity by simulating management practices

The retail sector has been identified as one of the biggest contributors to the productivity gap that persists between the UK, Europe and the USA. It is well documented that measures of UK retail productivity rank lower than those of countries with comparably developed economies. Intuitively, it seems likely that management practices are linked to a company’s productivity and performance. Significant research has been done to investigate the productivity gap and identify problems involved in estimating the size of the gap; for example the comparability of productivity indices, historical influences, general measurement issues, and varying sectoral contributions. Best practice guidelines have been developed and published, but there remains considerable inconsistency and uncertainty regarding how these are implemented and manifested at the level of the work place. Indeed, a recent report on UK productivity asserted that, “... the key to productivity remains what happens inside the firm and this is something of a ‘black box’”. Siebers and colleagues conducted a comprehensive literature review of this research area to assess linkages between management practices and firm level productivity. The authors concluded that management practices are multidimensional constructs that generally do not demonstrate a straightforward relationship with productivity variables. Empirical evidence affirms that management practices must be context specific to be effective, and in turn productivity indices must also reflect a particular organization’s activities on a local level to be a valid indicator of performance