Esmaeil Khedmati Morasae, 3 March 2013.
For at least 2 centuries Biomedical Model of Health (BMH) was the dominant perspective to Human Health and Disease. This model postulates that biological factors are the one and only causes of health and diseases. However, Onset of epidemiologic transition in diseases patterns brought with itself an omen and persistent pessimism to effectiveness of this model. This pattern implicates that, unlike communicable diseases, medicine is not completely able to cure chronic diseases and can only manage them. This matter along with some evidence that even breakthroughs in treatment of communicable diseases were mostly due to betterment in physical and social environments, and not to medical remedies, led to emergence of new models of health namely Social Determinants of Health (SDH).
This model has been around for centuries but no one took it seriously. The model holds that social factors (like social class, gender, education, occupation, discrimination, segregation and …) have a hefty hand in determination and production of health and diseases and even biological and behavioral factors are happening in a social context. World Health Organization (WHO) defines the SDH as "conditions in which people are born, grow, live, work and age, including the health system". These circumstances are shaped by the distribution of money, power and resources at global, national and local levels. WHO calls these conditions as "causes of the causes" that afflict or flourish the societies and they account for approximately 55% of diseases and health-related problems.
However, the recognition of social life as the most important determinant of health has had its problems mainly in terms of methodology. Health scientists have developed their specific methodology. This methodology allows them to separate causes of diseases from each other (regression-based models) and assign a (major or minor) role for each cause. These models are blind to interrelatedness of variables and even try to control for such relations. But, when it comes to social determinants of health and a complex world out there that determines our health (or one may dare to say our life) such limited models fail to fruit helpful insights. Systems and Complexity Science are suggested as methods that can overcome those methodological impasses and bring a new science of health for us. Indeed, a complexity approach may show us how social environment gets under our skin and shapes our health and wellbeing.
Sadegh Aliakbary, 25 February 2012.
Agent-based simulation of social networks.
Mohammad Ali Javidian, 18 February 2013.
Social choice theory or social choice is a theoretical framework for analysis of combining individual preferences, interests, or welfares to reach a collective decision or social welfare in some sense. We introduce a new criterion for social choice protocols called "social disappointment". Social disappointment happens when the outcome of a voting system occurs for those alternatives which are at the end of at least half of individual preference profiles.
Masoud Amoozgar, 22 December 2012.
Many important problems of complexity are related in one way or another with the presence of phase transition phenomena. Most complex systems are known to potentially display a number of different patterns of qualitative behavior or phases. Such phases correspond to different forms of internal organization and two given phases are usually separated by a sharp boundary, and crossing such a frontier implies a change in system-level behavior. Many of these transitions reveal important features of the system undergoing them. A good model of complex system should be able to predict the presence of such phases and their implications. Some of these changes can be catastrophic, and understanding them is crucial for the future of biodiversity or even our society.
Saman Feghhi, 3 March 2012.
In real life, we cannot always expect agents and authorities to have the same desires. Also when it comes to conflicts, agents always tend to break the authorities' norms to follow their own strategies. Hence we cannot expect strict norms to model an actual real life situation. In this paper we introduce punishment normative systems, based on multi-agent Markov chain processes (MMDPs). We try to extend the punishment idea to be also applicable on long-run and infinite strategies. We consider a version of the multi-agent model that is widely used in different situations and then provide an algorithm to find a fair normative system that distributes obligations equally on the agents, while still maximizes the social outcome of the system.
Pooya Khaloo, 29 November 2011.
This is the specification for the Trading Agent Competition – Supply Chain Management Game. A TAC SCM game consists of a number of “days” or rounds where six personal computer (PC) assembly agents compete for customer orders and for procurement of a variety of components. Each day, customers issue requests for quotes and select from quotes submitted by the agents, based on delivery dates and prices.
The game is representative of a broad range of supply chain situations. It is challenging in that it requires agents to concurrently compete in multiple markets (markets for different components on the supply side and markets for different products on the customer side) with interdependencies and incomplete information. It allows agents to strategize (e.g. specializing in particular types of products, stocking up components that are in low supply). To succeed, agents will have to demonstrate their ability to react to variations in customer demand and availability of supplies, as well as adapt to the strategies adopted by other competing agents. A game begins when one or more agents connect to a game server. The server simulates the suppliers and customers, and provides banking, production, and warehousing services to the individual agents.
The game continues for a fixed number of simulated days. At the end of a game, the agent with the highest sum of money in the bank is declared the winner.
Masoud Amoozgar, 25 February 2012.
The spread of rumors, which are known as unverified statements of uncertain origin, may lead to social disasters. If it would be possible to control a rumor, then this could be used to influence the desires of the society members. A computational model that includes rumor features and the way a rumor is spread among societys members, based on their desires, is therefore needed. The relationship between the homogeneity of the society and the convergence of rumors is discussed and our research result shows that the homogeneity of the society is a necessary condition for convergence of the spreading rumor.
Amirhossein Ghamarian, 13 September 2011.
Graph transformation is an expressive, intuitive and mathematically precise model of computation which is used for modelling and analyzing a wide range of systems. It is especially suitable for systems which contain entities linked by relations, such as network protocols, static structures as well as execution flow of software systems. Entities are represented by nodes and relations by edges between them. The computation is described in a reductive manner by changes in the entities and the relations between them. In this talk, the basic concepts of graph transformation, its mathematical formalism and some of its main applications will be explained. Furthermore, the graph transformation tool GROOVE will be used to demonstrate different concepts of graph transformation.