Table 4 Model’s parameters and their description
From: The diffusion of goods with multiple characteristics and price premiums: an agent-based model
Parameter | Description |
|---|---|
\(n_c\) | Number of consumer agents |
\(n_d\) | Number of dimensions. This determines the number of characteristics goods can have and that consumers can purchase |
\(\iota\) | Proportion of consumers with intention at \(t=0\). The model is preset so that a given proportion of consumers has the intention to purchase a characteristic right from the outset (they can be seen as the early adopters of the innovation diffusion literature) |
\(\rho\) | Random consumer links. Consumers are linked to their immediate neighbours in a circle lattice, following a regular-small world algorithm (Watts and Strogatz 1998). \(\rho\) defines the average number of additional \(L_c\) in the lattice |
d | Distance of influence. Consumers are influenced by their more or less immediate network according to the value of this parameter. A value of 1 means that only a consumer’s direct links are taken into account |
\(\tau\) | Influence threshold. A consumer with \(I_{i,a}=0\) on a given dimension a is likely to change this variable to \(I_{i,a}=1\) once a proportion \(\tau\) of \(M_i\) has reached \(A_{j,a}=1\) |
\(\kappa\) | Probability of influence. A consumer i whose \(\mathbf {J_i}\) reaches a given threshold of adoption regarding a goods’ characteristic a will modify its \(I_{i,a}\) with a given probability \(\kappa \in [0,1]\) |
\(\pi\) | Price premium. The higher price paid by consumers adopting a good containing one or more characteristics. It is defined as a percent value to be added on each additional characteristic present (\(\pi \in [0,1]\)) |
\(t^{max}\) | The duration of a simulation run. This time is fixed so as to ensure that all S-shaped curves of diffusion reach an equilibrium |