on 29-Jul-2022 (Fri)

Universal Framework for Agent based Models - 4 phases

(1) Initialization (2) Experience(3) Training(4) Application

In the first phase, Initialization, the important features of the agents and their environment need to be defined. Agents need some kind of input that can be both qualitative or quantitative. In the simplest case this is sensory input or general knowledge, but also other inputs that influence decision making are thinkable, like memory or individual preferences. One also needs to define the decision an agent needs to make in each time step. Most generally this will be the choice between several possible actions. Each agent also needs a target or a goal, mathematically expressed as a score or utility function that the agent wants to maximize. For simple economic systems, profit is a score that can easily be quantified and used as a target, but many other goals can be used, possibly including fairness [45,46] and social preferences [47,48]. Depending on the system, completely different properties can be used as goals (e.g. minimization of travel time for traffic systems) and they could be different for each agent. Once the system, the agents, the input for the agents, the decision and the goal of each agent is defined, the Initialization phase is finished. Note, that defining a utility function is in most cases easier than finding a rule set that leads to optimizing this function. Think about a game of chess: The utility function is easy to define (1 for a win, 0 otherwise), but finding a set of rules that gets the position of all pieces as input and a realistic move (probably even related to player skill) is nearly impossible. In that sense, the Initialization phase is relatively simple, when compared to traditional agent-based models.

Disadvantages of survey-based CX systems

4. Unfocused: As one executive at a large financial-services company put it, “The association between survey-based scores and business outcomes is not well understood, and, as a result, many parts of the organization simply claim a business impact from their CX initiatives with no real evidence.”

Agent-based modeling (ABM) has long been a common framework of choice for studying aggregate, or emergent, properties of complex systems as they arise from microbehaviors of a multitude of agents in social and economic contexts [7,30,35]. ABM appears well-suited to policy experimentation of just the kind needed for the rooftop solar market. Indeed, there have been several attempts to develop agent-based models of solar adoption trends [14,32, 38]. Both traditional agent-based modeling, as well as the specific models developed for solar adoption, use data to calibrate aspects of the models (for example, features of the social network, such as density, are made to match real networks), but not the entire model. More importantly, validation is of- ten qualitative, or, if quantitative, using the same data as used for calibration. The weakness of validation makes those models less eligible as a reliable policy experiment tool.

Our proposal of calibration at the agent level, in contrast, enables us to leverage state-of-the-art machine learning techniques, as well as obtain more reliable, and interpretable, models at the individual agent level. Recently, in field of ecology and sociology, there is rising interest to combine agent-based model with empirical methods [24]. Biophysical measurements, i.e., soil properties and socioeconomic surveys are used by Berger and Schreinemachers [4] to generate a landscape and agent populations which are consistent with empirical observation by Monte Carlo techniques. Notice that this is quite different application from ours, since we do not need to generate an agent population; rather we instantiate our multi-agent simulation with learned agents.

We now present our general framework for data-driven agent-based modeling (DDABM), which we subsequently apply to the problem of modeling residential rooftop solar diffusion in San Diego county, California. The key features of this framework are: a) explicit division of data into “calibration” and “validation” to ensure sound and reliable model validation and b) automated agent model training and cross-validation. In this framework, we make three assumptions. The first is that time is discrete. While this assumption is not of fundamental importance, it will help in presenting the concepts, and is the assumption made in our application. The second assumption is that agents are homogeneous. This may seem a strong assumption, but in fact it is without loss of generality. To see this, suppose that h(x) is our model of agent behaviour, where x is state, or all information that conditions the agent’s decision. Heterogeneity can be embedded in h by considering individual characteristics in state x, such as personality traits and socio-economic status, or, as in our application domain, housing characteristics.

We now present our general framework for data-driven agent-based modeling (DDABM), which we subsequently apply to the problem of modeling residential rooftop solar diffusion in San Diego county, California. The key features of this framework are: a) explicit division of data into “calibration” and “validation” to ensure sound and reliable model validation and b) automated agent model training and cross-validation

innocuous

adjective

UK /ɪˈnɒkjuəs/ US

not likely to upset or harm anyone

Recently, in field of ecology and sociology, there is rising interest to combine agent-based model with empirical methods [24]. Biophysical measurements, i.e., soil properties and socioeconomic surveys are used by Berger and Schreinemachers [4] to generate a landscape and agent populations which are consistent with empirical observation by Monte Carlo techniques. Notice that this is quite different application from ours, since we do not need to generate an agent population; rather we instantiate our multi-agent simulation with learned agents

acronym

noun [ C ]

UK /ˈækrəʊnɪm/ US

a word made from the first letters of other words

akronim, skrótowiec

AIDS is the acronym for 'acquired immune deficiency syndrome'.