In 1963-71, a group of people, myself included, formulated and perfected a new approach to physics problems, which eventually came to be known under the names of scaling, universality, and renormalization. This work formed the basis of a wide variety of theories ranging from its starting point in critical phenomena, and moving out to particle physics and relativity and then into economics and biology. This work was of transcendental beauty and of considerable intellectual importance.

We read newspapers and watch TV every day. There are many issues and many controversies. Since media are free, we can hear arguments from every possible side. How do we decide what is wrong or right? The first condition to accept a message is to understand it; messages that are too sophisticated are ignored. So it seems reasonable to assume that our understanding depends on our ability and our current knowledge. Here we show that the consequences of this statement are surprising and funny.

Do different field of knowledge require different research strategies? A numerical model exploring different virtual knowledge landscapes, revealed different optimal search strategies. Trend following is maximized when the popularity of new discoveries determine the number of individuals researching it. This strategy works best when many researchers explore few large areas of knowledge. In contrast, individuals or small groups of researchers are better in discovering small bits of information in dispersed knowledge landscapes. The best technique for all situations simulated, is to adjust the number of researchers needed to explore a knowledge cluster according to the opportunities and the level of crowding in that cluster.

Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. We choose china’s listed ICT industry to make empirical analysis. Our main findings are: a) technology progress is the order parameter which dominates industry system evolution. Moreover, industry convergence is the control parameter which is influenced by technology progress; b) Development of technology progress is the core factor for causing evolution of industry system, and industry convergence is the outcome of technology progress; c) Especially, it is important that the dominated role of technology progress will be sustained, even though in the environment of convergence, companies also need focus on self-innovation, rather than only adapt to the new industry evolution trend.

Humans are fundamentally social. They have progressively dominated their environment by the strength and creativity provided by and within their grouping. It is well recognised that human groups are highly structured, and the anthropological literature has loosely classified them according to their size and function, such as support cliques, sympathy groups, bands, cognitive groups, tribes, linguistic groups and so on. Recently, combining data on human grouping patterns in a comprehensive and systematic study, Zhou et al. identified a quantitative discrete hierarchy of group sizes with a preferred scaling ratio close to 3 , which was later confirmed for hunter-gatherer groups and for other mammalian societies. Using high precision large scale Internet-based social network data, we extend these early findings on a very large data set.

We study how firms select partners using a large database of publicly announced R&D alliances over a period of 25 years. We identify, for the first time, two distinct behavioral strategies of firms in forming these alliances. By reconstructing and analysing the temporal R&D network of 14,000 international firms and 21.000 publicly announced alliances, we find a “universal” behavior in firms changing between these strategies. In the first strategy, newcomers and nodes of low centrality initially establish links to nodes of similar or higher centrality.

We present a model that explores the influence of persuasion in a population of agents with positive and negative opinion orientations. The opinion of each agent is represented by an integer number k that expresses its level of agreement on a given issue, from totally against k=−M to totally in favor k=M . Same-orientation agents persuade each other with probability p , becoming more extreme, while opposite-orientation agents become more moderate as they reach a compromise with probability q .

The compartmental models used to study epidemic spreading often assume the same susceptibility for all individuals, and are therefore, agnostic about the effects that differences in susceptibility can have on epidemic spreading. Here we show that–for the SIS model–differential susceptibility can make networks more vulnerable to the spread of diseases when the correlation between a node’s degree and susceptibility are positive, and less vulnerable when this correlation is negative. Moreover, we show that networks become more likely to contain a pocket of infection when individuals are more likely to connect with others that have similar susceptibility (the network is segregated).

We study a resource utilization scenario characterized by intrinsic attractiveness. We consider a system of many restaurants where customers compete, as in a game, to get the best services out of many choices using iterative learning. Results for the case with uniform attractiveness are reported. When attractiveness is uniformly distributed, it gives rise to a Zipf-Pareto law for the number of customers. We perform an exact calculation for the utilization fraction for the case when choices are made independent of attractiveness. A variant of the model is also introduced where the attractiveness can be treated as a fitness to stay in the business. When a restaurant loses customers, its fitness is replaced by a random fitness. The steady state fitness distribution is characterized by a power law, but the distribution in number of customers is still given by power law, implying the robustness of the model. Our model serves as a paradigm for city size distribution and the emergence of Zipf law.

The weighted and directed network of countries based on the number of overseas banks is analyzed in terms of its fragility to the banking crisis of one country. We use two different models to describe transmission of shocks, one local and the other global. Depending on the original source of the crisis, the overall size of crisis impacts is found to differ country by country. For the two-step local spreading model, it is revealed that the scale of the first impact is determined by the out-strength, the total number of overseas branches of the country at the origin of the crisis, while the second impact becomes more serious if the in-strength at the origin is increased. For the global spreading model, some countries named “triggers” are found to play important roles in shock transmission, and the importance of the feed-forward-loop mechanism is pointed out. We also discuss practical policy implications of the present work.