Many social simulations can be represented using mobile-agent-based model in which agents moving around on a given space such a evacuations, traffic flow and epidemics. Whole planet simulation with billions of agents at microscopic level helps mitigate the global crisis. It introduces new technical challenges such as processing and migrating many agents and load balancing among hundreds of machines. To overcome these challenges, well-designed software architecture of a simulator is essential. In this research, we proposed agent-basedcomplex cellular automata architecture (ABCCA) and studied the performance and scalability of two cell-based processing models,through simple traffic flow simulation on multi-core distributed system. The experiments show that the computation speedup can be achieved by reducing granularity of tasks and processing only active spaces. We achieved running the traffic flow simulation with one billion of agents in almost real time on 1,536 CPU cores of total 128 machines of TSUBAME supercomputer.
Computational social science is only as good as the models used and the data analysed to describe and to understand social phenomena.
So, when is a model good enough? And how can the available data be used to calibrate a model? In this talk, I will illustrate these problems by focusing on emotional influence. Different from opinions, emotions are short-lived psychological states that strongly bias individual behavior. Following Russell (1980), emotions can be classified along the dimensions of valence (the pleasure associated with emotions) and arousal (the degree of activity induced by emotions). We can quantify the emotions of individuals who, for example, participate in an online chat, by surveying their subjective response or by providing a sentiment analysis of the text they read and write. But how can this be linked to a model?
We have developed an agent-based modeling framework where the dynamics of individual valence and arousal and the communication between agents is explicitely modeled. For the emotional response of agents, we can test different assumptions (a) by fitting these to the observed subjective response, and (b) by comparing the model output to the observed collective behavior. We will provide different examples (communication in online chats, product reviews, emotional cascades) to demonstrate that the agent-based models can remarkably well reproduce the real behavior of users in online social media.
Unlike conventional macroeconomic models which stress forward looking behaviour by far-sighted and rational, often representative, agents at the expense of the "plumbing" (i.e. the inter-connections) of an actual economy, ABMs have the advantage of simplifying behavior at the individual level by assuming that agents follow given but evolving rules-of-thumb, and this allows them to explore the multiplicity of agent types and their set of inter-connections in far greater detail. ABM typically assume large populations of heterogeneous agents, specifying agents individual behaviour, the environment, and modes of interaction. Agents are not only heterogenous and interacting but also adaptive; they have different circumstances, different histories and adapt continuously to the overall situation they create. To handle real-world features, it is essential to permit agents to engage in comprehensive forms of learning that include inductive reasoning (experimentation with new ideas) as well as aspects of reinforcement learning, social mimicry, and forecasting of future events. In ABM, agents can range from passive automatons with no cognitive function, to active data-gathering decision makers with sophisticated learning capabilities.
In this talk I will review some mechanisms of agents learning and expectation formation that provide the micro-fundation of economic and financial ABMs, focusing on their stabilizing/destabilizing effects on the resulting macro-dynamics of financial, credit and goods markets.
We are glad to invite you to take part at the conference dinner.
The dinner will take place at Cantieri Marconi in Lungotevere Dante 273. A shuttle bus will be available in front of CNR building to reach together the restaurant.