Netlogo random float
Then, a Cellular Automaton model was developed and its initial condition was inherited from the results of the first market research response values and evolved to simulate human interactions that led to the values of the second market research, without explicitly imposing causality rules.
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#NETLOGO RANDOM FLOAT PLUS#
The model used market research with real people in two different times: one at time zero and the second at time zero plus 4 months (longitudinal market research). In my thesis, I used a Cellular Automata (CA) model to simulate human interactions that happen in the real world (Zimbres, Oliveira, 2009). In my final project at the above mentioned course I developed a social network based in Cellular Automata as in Zimbres et al. This affects the way you are doing science and the way you handle and understand the findings of your research. Sometimes you already have a hypothesis that you want to accept or reject. Sometimes you are developing an exploratory model, to understand the underlying dynamics of a system. So, what to expect as an output from an agent-based model ? It depends. Inputs are the independent variables of the model that will be applied to agents/environment. As it is a discrete event simulation, you must also define what happens at each time step. When you create an agent-based model, you must define the types of agents that compose the system, their rules of behavior (actions), properties and environment interaction. Also, you are not trying to add all possible rules of the system to a given individual, as the basic assumption of ABMs is the simplicity. You are not trying to generalize, differently from Machine Learning algorithms. In fact, ABMs can be thought of as a third way of doing science, beyond induction and deduction (Axelrod, 1997). Sometimes the outcome may be surprising, counter intuitive and this may generate the need for a new understanding of concepts taken for granted, generating a new theory (Zimbres, 2006).ĪBMs are sensitive to initial conditions, and are often non-linear.
#NETLOGO RANDOM FLOAT VERIFICATION#
This approach facilitates epistemological validity, given that it requires rigorous internal validity and construct validity as well as the verification of the implemented model according to the concepts being modeled. That’s why ABM is called science from the bottom up (Axelrod, 1997 Epstein, Axtell, 1996). In Agent-Based Modeling, rather than map X (causes) to Y (effects), we are more interested in understanding the processes happening between cause and effect. He may simply interact, make choices, achieve a goal or even try to maximize his utility (Kahneman, Tversky, 1979 Kim, Matson, 2016). The model runs parallel updates of individuals in a discrete manner, and an agent may or may not have consciousness about the choices he made in the past. This emergent outcome is not necessarily related to the initial conditions. The MBA that relates the micro and macro levels is a relevant research tool for sociologists (Macy, Willer, 2002) through which one can perform abstractions.
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From simple and localized rules at the individual level, we can see the appearance of emergent properties of a given system (Shelling, 1978 Cederman, 2003 Axelrod, Tesfatsion, 2005 Epstein, Axtell, 1996 Sawyer, 2003-2004 Hegselmann, Flache, 1998 Wolfram, 2002). In ABMs we start from simple rules to generate complex patterns, where micro behaviors cause macro phenomena. The observation in this context is a change of paradigms in an attempt to understand our world, as we realize that the laws governing the whole cannot be deduced simply from the mere observation of the details of its constituent parts (Vicsek, 2002). In complex systems, processes occur simultaneously and the complex behavior of the whole system depends on its sub-units in a non-trivial way. William Rand, from North Carolina State University.Īgent-Based Modeling (ABM) is a methodology to simulate phenomena according to complexity principles. The content is awesome and is taught by Dr. Lately I’ve been doing a course Introduction to Agent-Based Modeling at Complexity Explorer, a teaching platform from the Santa Fe Institute.