Game Development. Critical thinking.
Revisiting influence analysis. Once variables (also called factors and drivers according to authors) have been identified – and in our case mapped, most foresight methodologies aim at reducing their number, i.e. keeping only a few of those variables. Indeed, considering cognitive limitations, as well as finite resources, one tries obtaining a number of variables that can be easily and relatively quickly combined by the human brain.
Furthermore, considering also the potential adverse reactions of practitioners to complex models, being able to present a properly simplified or reduced model (however remaining faithful to the initial one) is most often necessary. When the foresight methodology does not include links between variables, thus if we don’t have a graph or a map, then the way to select variables is by ranking them according to specific criteria. Among the criteria most used, one finds likelihood and impact, or impact and uncertainty (i.e. one does not know how the variables will evolve). Influence graph. Variables, values and consistency in dynamic networks.
In this post we shall explain and discuss the methodological background that allows us to set the criteria for Everstate – or for any country or issue chosen for foresight analysis – as exemplified in the post “Everstate’s characteristics.” Values must now be attributed to each selected initial criterion, as would be done with Morphological Analysis.
However, here, those values will be those corresponding to the present and not to the future (as in Morphological Analysis). If the foresight were done about a specific known country, then it would be (relatively) easy to attribute real values for the selected criteria. In our case, those values will help us creating Everstate, putting flesh on our ideal-type and starting making it look real. A similar approach could be used for any issue. Even if we are working with an ideal-type, we nevertheless must remain in the realm of the plausible and thus values must not be far-fetched. Constructing a foresight scenario’s narrative with Ego Networks. In many foresight methods, once you have identified the main factors or variables and reach the moment to develop the narrative for the scenarios, you are left with no guidance regarding the way to accomplish this step, beyond something along the line of “flesh out the scenario and develop the story.”* Here, we shall do otherwise and provide a straightforward and easy method to write the scenario.
We shall use the dynamic network we constructed for Everstate – or for another issue – and the feature called “Ego Network” that is available in social network analysis and visualisation software to guide the development and writing of the narrative. An ego network is, basically, the network that surrounds or is centered upon a single variable or node, called, in this case, an “ego.” This network will be the backbone of our narrative. The depth of an ego network is the length of the path between the selected ego and a linked node or variable.
With one ego network Ego networks, an analyst’s weapon.
Social Network Analysis. Complexity, science. Structural diversity in social contagion | Papers. Structural diversity in social contagion. Author Affiliations Edited by Ronald L. Graham, University of California at San Diego, La Jolla, CA, and approved February 21, 2012 (received for review October 6, 2011) Abstract The concept of contagion has steadily expanded from its original grounding in epidemic disease to describe a vast array of processes that spread across networks, notably social phenomena such as fads, political opinions, the adoption of new technologies, and financial decisions. Traditional models of social contagion have been based on physical analogies with biological contagion, in which the probability that an individual is affected by the contagion grows monotonically with the size of his or her “contact neighborhood”—the number of affected individuals with whom he or she is in contact.
Footnotes Freely available online through the PNAS open access option. What Fuels the Most Influential Tweets? - Jared Keller - Technology. The number of followers you have and the exact wording matter less than you think. What makes a difference is having the right message for the right people. Visualization of meme diffusion related to the Arab Spring and the 2011 uprisings from the #Egypt hashtag, which shows strong connectivity and many users linked to one another to form a dense cluster. [Courtesy of Indiana University] "Influence" doesn't necessarily mean what you think it does. In the age of the social-media celebrity, a glut of Twitter followers or particularly pugnacious sampling of pithy updates are often the hallmarks of an influencer. In a new paper entitled Competition Among Memes in a World With Limited Attention, Indiana University researchers Lillian Weng, Alessando Flammini, Alessando Vespignani, and Filippo Menczer analyzed 120 million retweets connected to 12.5 million users and 1.3 million hashtags in order to model how information (as discrete units, or memes) disperses on the social network.
Symposium on New Science-Based Tools for Anticipating and Responding to Global Crises (Paris, 18 April 2012) Symposium to celebrate the 20th anniversary of the OECD Global Science Forum (GSF) and the 100th Session of the OECD Committee for Science and Technological Policy (CSTP) Paris, 18 April 2012 Background | Objectives | Draft Agenda Registration and Contact | Practical Information Summary Record Background Our world is currently passing through a series of crises and challenges of natural and man-made origin. The crises are complex in nature, global in scale, were largely unanticipated, and originated from a series of cascading, inter-related events.
Governments are struggling to mitigate their effects, and are searching for the best recovery policies. Science and technology are among the tools that governments have at their disposal, and these have been exploited extensively in the past. The potential role of S&T in addressing crises has already been the focus of previous OECD activities: Objectives The expected outcomes included: Draft Agenda (PDF in English ) Moderator: Respondents: Keynote:
COMPLEX 2012 - 2nd International ICST Conference on Complex Sciences: Theory and Applications | CxConferences.
EPJ Data Science - a SpringerOpen journal | CxAnnouncements. Sensing the City: Mapping London’s Population Flows. I recently had the pleasure of presenting at the first Data Visualisation London Meetup event where I spoke about some of work we do at UCL CASA. A fair chunk of the slides were movies so I thought it best to stick them in a blog post. If you like what you see you can sign up for the CASA masters course or check out our other blogs. First up was my interactive surname map of London.
I used this to demonstrate that “Big Data” (the general theme of the meetup) is nothing new (we have collected large- scale population data for over a century) and that we can use visualisation to demonstrate complex data. Next, was the now famous animation of London’s transport flows produced by Joan Serras. I then went on to say that we can begin to build more sophisticated maps of public transport by utilising routing algorithms. I then showed a couple of top-secret visualisations produced by Jon Reades and others at CASA.
And other cities (below) to see how people utilize the schemes. Group Foraging in Dynamic Environments | Papers. [1204.3673] Group Foraging in Dynamic Environments.