*7.2. Model Parameters and Crowd Sensing*

Creation of virtual digital twins is the aim of the crowd sensing. The crowd sensing is performed with chat bot agents. One stationary agent is operating on a user device, e.g., a smartphone, and another mobile agent is responsible for performing a survey (either participatory with a former negotiation or opportunistic ad-hoc). The results of the survey, a set of questions, are used to derive the following simulation model parameters, shown in Definition 2.

**Definition 2.** *Sakoda model parameters.*

```
type parameters = { 
 group : string "a"|"b"|.., 
 social-distance: number [1-100], 
 mobility-distance: number [1-100], 
 social-attitudes : [saa,sab,sba,sbb] | number [][], 
 mobility : number [0-1], 
 position : {x:number,y:number}, 
 destination : {x:number,y:number} 
}
```
The *group* parameter sorts the user in one of two classes *a*/*b*, the *social-distance* parameter is an estimation of the social interaction distance, the *social-attitudes* parameter is the *S* vector, but limited to a subset of all possible *S* vector combinations (discussed below), and the *mobility* parameter is a probability to migrate from one place to another. The position (in Cartesian coordinates) is derived from the living centre of the user (global position data, GPS) and mapped on the simulation world (*x*,*y*). The *S* vector parameter determines the spatial social organisation structure. Typical examples of the *S* vectors with relation to social behaviour are [36]:


The crowd sensing extends the simulation with the following dynamics and changes:


The dynamics in the simulation world can be backpropagated to the real world by chat bot agents, too, delivering information and opinions formed by the artificial entities. Among classical surveys, chat bots can perform manipulation by distributing biased information or opinions via the chat dialog interface.
