Simulating the Urban Economy: Experiments with input-output techniques: Volume 43 (City)

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In this case, some treatment is applied on the isolated system and the effect of the changed behavior of agents, compared to precedent run, without treatment control. In social sciences, the isolation of a system is not often possible or ethically problematic and therefore the advantages of Agent-based modeling are helpful in experiments.

Further, also the costs of a conducted. It can happen that in this kind of model validity of conclusion can be reduced, as some of the features are not included. The most interest is in the question of how well data fits the equation and less on the real consequence of the equation. Often these mathematical models are not very helpful in clarifying why one variable is related to the other. The implemented Agent-based model for the attractiveness of Lugano area has an equation-based character, as the behavior of integrated agents is guided by a core equation.

The model also has scale model character, as it reduces in size and details the reality of the Lugano area. Then, for the removal of some not quantifiable and complicating factors, it is also an ideal type model in a certain sense. Less it is to be consider as an analogical model. Further, in the simulation environment agents are programmed so to react to the computational environment where they are located in. The Agent-based model provides in addition the possibility of interaction between different agents and enables them to share information.

The environment instead is defined as a virtual world, in which agents move and act. In the Lugano area attractiveness model, the environment is designed as geographic space and several indicators characterize each of the different places. The model has not a spatially explicit character, as municipalities of the different zones do not have a fixed location in the space, but are distributed randomly.

Another feature is to connect agents with network links. The time dimension in this configuration is modeled in discrete time steps. In this way, each of these time steps has the same duration. However, three considerations have to be kept in mind for. Therefore, some attention is required when programming the sequence of actions. There are three possible ideas how to work around this weakness of the system: first is to invoke each agent in a sequential order and so define a sequential asynchronous execution that puts in a predefined order the actions of every agent.

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A second alternative is to define a random asynchronous execution and so agents interact in a randomly chosen order with each other. The advantage of this method is that by running several times a model, the problem of time order can be solved.

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The third option is to simulate in a synchronous execution in which agents are ordered in a convenient way and all interactions are calibrated so that all inputs to agents are completed before all outputs are. For the Lugano area model, synchronicity does not have such a high importance as the interaction of firms with the municipalities depends on the actual physical distance of a firm to a certain town.

The concept of "first come, first serve" is, therefore, applied and for the rest, the random order of how the system is setup and the multiple repetitions resolve the remaining problematic through random asynchronous execution. This does not have importance for the Lugano area model, as it is completely time-based. Often in the real world, actions happen less quickly and the starting point of a determined problem is later than in simulation.

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According to theory, no exact solution to overcome this problem is provided. For the attractiveness of the Lugano area Agent-based model, this effect appears for the fact that firms move much quicker and in a more sensible way in the geographic space than in reality.

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It is, therefore, important to interpret the trend of localized firms over time, from multiple simulations to figure out results. Agent-based models can also be applied in urban space planning. As an example, it is possible to integrate a simulation model with the Geographical Information System GIS , to explore urban dynamics.

In this context, the advantages of an Agent-based model are that the individual's actions and the behaviors over time were. More specifically, in this model, objects, for example, houses and persons, are coupled through spatial-specific objects with their functions and situations.

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  • So for example, firms and people can be assumed as non-fixed objects, while parks are fixed objects for example. Non-fixed objects are assigned to have transition rules, which guide their behavior of moving in the space. This paper develops an ABM of localization decisions taken by firms within an urban agglomeration. It is not, however, the first to do so. Tsekeris and Vogiatzoglou for example were establishing a multi-regional model in order to simulate the behavior of workforce as well as the decision making of relocating firms.

    Their model also accounts for characteristics that drive industries in their location decision, taking into account agglomeration forces. In another model, Xie et al. The units of analysis define the development of the land in the region. The created model is based on a first scale analysis, which includes the growth of different spatial aggregates, remedying on different spatial aggregates.

    The agent-based model then is created in a way that allows matching new patterns of urbanization against behavior of households. A third agent-based model that provides useful background information is the one published by Baruffini It generates an artificial labour market which describes the dynamic matching between labour supply and demand and how it is affected by specific changes in policy and social parameters. His model is effective in testing different scenarios useful in the evaluation of different policy setting, allowing to define model parameters on worker's nationality, agent's wealth, agent's strategy, worker's "learning constraint", the flexibility of the labour market and differences between sectors.

    The inputs gained from theory are helpful and to be kept in mind for the actual design of the Agent-based model that describes the attractiveness of the Lugano conurbation's participant municipalities for firms of different branches. In the following part, the ideas and theory are connected to each other and an Agent-based model to simulate the movement of firms in the geographic space is designed.

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    The model is programmed into NetLogo, a program specifically designed to implement agent-based modelling. Following Dahlem ABM documentation guidelines [19], the features of the model are structured as follows. The model is structured so that the constructed space has geographic character, representing the total 45 municipalities of the Lugano conurbation. This virtual conurbation contains a number of municipalities, called NumPatches, and each of this makes part of one of the five defined regions. Each of these municipalities has assigned an initial value for the variables of:.

    Values of these variables are kept constant for the entire simulated first year, so to give the initially randomly distributed firms the chance to find an attractive location. From day on these variables change every year, continuing the trend of the past five years. In order to test the universality and functionality of the model, in a first step, the distribution of firms among the municipalities during the fixed period of the first days in the model was compared with the real relative specialization of each region.

    The specialization value was calculated for every single municipality on the basis of the data of the distribution of firms in the Lugano conurbation. Total Supply Energy, waste, etc. A high concentration of a branch in a municipality can be both, a factor of increasing attractiveness that makes further enterprises of this branch move towards this municipality or have the opposite effect and reject firms from locating there, because with the growing number of firms also the competition increased. Which of this effect is more important depends on again in the critical factors that conduct a branch to success.

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    • On one hand, a location near competitors can be attractive, so that talented persons are available in the area and the company can gain profits from the presence of know-how. In addition, the presence of important partners can be a favorable factor to attract further firms. Factors as the before mentioned, prevail mainly for firms that operate internationally and whose output markets are abroad.


      An example could be watch-and-clock industry or even biochemistry firms. Industries operating and selling mainly on the local market are more likely to choose locations further from competitors' ones. Especially when the business is location-bound and not mobile, so that direct competition is created. Examples could be hotels if a too high concentration in the location is reached or small food shops.

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      Others, even being location. Examples here can be found in the retail segment, banks and insurances but also in company-oriented services. More important than avoiding competition situation to them is to appear as strong as possible in all locations. The relative importance of certain branches in the Lugano conurbation is the defining factor that provided the model verification concerning the decision of localization for a firm. The number of municipalities making part of each region is defined, by the reality and the categorization made in the first part of this work.