The last day of the event. First, keynote talk by David G. Green, from Monash University, Australia “Of Ants and Men”. Golden rules – same as ants – humans do not “see” how all the interactions will turn out. Unconscious social trends: example left-handers in sport. Side effects of technology – refrigerator – no need to shop daily, less social interaction, more free time, more women work, need for child care, more money spending, stored food longer, large scale storage, supermarkets, small businesses died. Internet banking and commerce: no need for human contact; mobile phones – teen text culture, french riots. Stock market crashes are human phenomena – the actions are not interactive, but programmed into the system through automated scripts and processes. Unexpected effects of having automation. Some effects of the internet: the big get bigger: speads english language, spreads western (US) culture. The small can survive too – small, speciaised groups can form. The social service agencies do not operate for the common good – they operate driven by individuals who build their own individual empires – the bigger the empire becomes, the more important they become and the easier it is to get funding and the snowball effect is rolling. However, through sharing and opening up your data and results more research can be done. Sharing empowers everyone – but it is difficult due to social and personal issues. Some online communities enforce sharing (e.g. genetic and biotechnology research – publishing data into public repositories is made compulsory). Why people do not cooperate: ignorance, lack of time/resources, no benefits, fear, self-interest: power,commerce. Cooperation can be promoted through appropriate funding structures. People are like ants – we just respond to stimuli and environment. Social networks refer to many kinds of agents not only people. Complexity exhibits emergent features, e.g. flock of birds. Concept of positive feedback is important – it drives the global dynamics of a complex systems. The concept of criticality – the connectivity avalanche – variance is the highest in the critical point. The concept of social boolean networks. Natural size of a grooming group: 30-60. Speech increased the social groupsize to 100-150. Dunbar’s hypothesis. Can media influence everybodys opinion? Yes – in the long run media always win. The tight social interaction can initially slow down the influence of media. However, after certain critical point, the adoption of media message accelerates.
Second talk of the day: Tania G. Leishman and David G. Green “Self-organization in Simulated Social Networks. Presented by David. Variable network structure, effects of adding/removing links. Set of binary properties produces a completely different network structure to a multi-valued properties.
Michael Winikoff, RMIT (and Stephen Cranefield, Otago) – “Eliciting expectations for monitoring social interactions”. Expectation monitoring and checking relates to reputation mechanism and making many things verifiable. Linear temporal logic usually used to specify the expectations. Two formalisms – in a graphical formalism, or in textual formalism. In the article authors propose generation of the formula from provided examples. Inspired by the software engineering paradigm of “programming by examples”. Problems of up to length 4 tend to take few minutes to be solved.
Fa Martin-Niemi, Richard Greatbans, SME knowledge management through social networking and leveraging storytelling in management. Otago University. Case study – small NZ companies ith international offices, and duel growth stratey: increase number of offices, and new practices.
Malcolm Shore, Data in Social Network Analysis.
Stephanie Broege, Otago, Social Networks and the Use of Mobile Phones. The experimental study of going on without mobile phone for 48hrs – out of 100 participants only 6 could achieve the desired target. People are becoming more selective in their network of friends. “choose the person who most closely satisfies their preference at any given moment (Puro, 2002:124). High percentage of mobile phone users especially in the 18-28 age bracket.
Sarah Stewart, using social netwoking for e-mentoring. Practicing midwife. Problems with 50+ internet users in engaging in computer mediated communication and social networking. Major barrier in adaptation of 3D like internet environments. Difficulties in getting engagement from general public.
Other thoughts: there is an abundance of social networking tools out there. Some voices express their opinion that the winner take all, and in each niche there will be only one major player dominating the particular niche. I agree, although I suspect it will follow power law distribution, so there will be few major ones, with a long tail of other, small, customised and more contextualised. Another thought- if the professional knowledge also follows power law like distribution across a population, then there is usually few domain/niche experts, followed by a long tail of people that know some, but not as much. Jumping onto higher levels of knowledge and understanding would require exponentially more effort then. Is that true?
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