Social groups can be remarkably smart and knowledgeable when their averaged judgements are compared with the judgements of individuals. Already Galton [Galton F (1907) Nature 75:7] found evidence that the median estimate of a group can be more accurate than estimates of experts. This wisdom of crowd effect was recently supported by examples from stock markets, political elections, and quiz shows [Surowiecki J (2004) The Wisdom of Crowds]. In contrast, we demonstrate by experimental evidence (N = 144) that even mild social influence can undermine the wisdom of crowd effect in simple estimation tasks. In the experiment, subjects could reconsider their response to factual questions after having received average or full information of the responses of other subjects. We compare subjects' convergence of estimates and improvements in accuracy over five consecutive estimation periods with a control condition, in which no information about others' responses was provided. Although groups are initially "wise," knowledge about estimates of others narrows the diversity of opinions to such an extent that it undermines the wisdom of crowd effect in three different ways. The "social influence effect" diminishes the diversity of the crowd without improvements of its collective error. The "range reduction effect" moves the position of the truth to peripheral regions of the range of estimates so that the crowd becomes less reliable in providing expertise for external observers. The "confidence effect" boosts individuals' confidence after convergence of their estimates despite lack of improved accuracy. Examples of the revealed mechanism range from misled elites to the recent global financial crisis.collective judgment | estimate aggregation | experimental social science | swarm intelligence | overconfidence

The current economic crisis illustrates a critical need for new and fundamental understanding of the structure and dynamics of economic networks. Economic systems are increasingly built on interdependencies, implemented through trans-national credit and investment networks, trade relations, or supply chains that have proven difficult to predict and control. We need, therefore, an approach that stresses the systemic complexity of economic networks and that can be used to revise and extend established paradigms in economic theory. This will facilitate the design of policies that reduce conflicts between individual interests and global efficiency, as well as reduce the risk of global failure by making economic networks more robust.

Recent research has highlighted limitations of studying complex systems with time-varying topologies from the perspective of static, time-aggregated networks. Non-Markovian characteristics resulting from the ordering of interactions in temporal networks were identified as one important mechanism that alters causality and affects dynamical processes. So far, an analytical explanation for this phenomenon and for the significant variations observed across different systems is missing. Here we introduce a methodology that allows to analytically predict causality-driven changes of diffusion speed in non-Markovian temporal networks. Validating our predictions in six data sets we show that compared with the time-aggregated network, non-Markovian characteristics can lead to both a slow-down or speed-up of diffusion, which can even outweigh the decelerating effect of community structures in the static topology. Thus, non-Markovian properties of temporal networks constitute an important additional dimension of complexity in time-varying complex systems.

We investigate the motion of Brownian particles which have the ability to take up energy from the environment, to store it in an internal depot, and to convert internal energy into kinetic energy. The resulting Langevin equation includes an additional acceleration term. The motion of the Brownian particles in a parabolic potential is discussed for two different cases: (i) continuous take-up of energy and (ii) take-up of energy at localized sources. If the take-up of energy is above a critical value, we found a limit-cycle motion of the particles, which, in case (ii), can be interrupted by stochastic influences. Including reflecting obstacles, we found for the deterministic case a chaotic motion of the particle. [S0031-9007(98)06328-5] PACS numbers: 05.40. + j, 05.45. + b, 05.60. + w, 87.10. + eActive motion is based on energy consumption. For biological systems, an external supply of energy is crucial, e.g., to maintain metabolism and to perform movement [1]. For a spatially inhomogeneous supply of energy, the organism needs to store energy internally, in order to overcome periods of starvation, e.g., during the search for new sources. But even provided the homogeneous supply of energy, the organism needs to convert the energy taken up from the environment into kinetic energy. Dependent on the level of biological organization, the take-up, storage, and conversion of energy is a rather complex process.In the following, we consider the motion of microscopic biological objects, such as cells or bacteria, which can be sufficiently described by a Langevin dynamics. Stochastic differential equations have long been used to describe the motion of organisms [2,3]. In order to derive a simplified model of active biological motion, we study Brownian particles with an internal energy depot. The motion of simple Brownian particles in a space-dependent potential, U͑r͒ can be described by the Langevin equation:where g 0 is the friction coefficient of the particle at position r, moving with velocity y. F ͑t͒ is a stochastic force with strength S and a d-correlated time dependenceRecently, Brownian motion models attracted much attention for describing nonequilibrium transport on the microscale [4]. In addition to the dynamics described above, the Brownian particles discussed here are active particles [5] to the effect that they have the ability to take up energy from the environment and to store it in an internal depot, which is considered a new element of the model. Further, the particles are able to convert internal energy into kinetic energy. Considering also internal dissipation, the resulting balance equation for the internal energy de-pot, e, of an active particle is given by d dt e͑t͒ q͑r͒ 2 c e͑t͒ 2 d͑y͒ e͑t͒ .( 3) q͑r͒ is the space-dependent take-up of energy and c describes the internal dissipation assumed to be proportional to the depot energy. d͑y͒ is the rate of conversion of internal into kinetic energy which should be a function of the actual velocity of the particle. A simple ansatz for d͑y͒ reads: d͑y͒ d 2 y 2 ; ...

Functional alterations are first signs of a starting pathological process. A device that measures parameter for the characterization of the metabolism at the human eye-ground would be a helpful tool for early diagnostics in stages when alterations are yet reversible. Measurements of blood flow and of oxygen saturation are necessary but not sufficient. The new technique of auto-fluorescence lifetime measurement (FLIM) opens in combination with selected excitation and emission ranges the possibility for metabolic mapping. FLIM not only adds an additional discrimination parameter to distinguish different fluorophores but also resolves different quenching states of the same fluorophore. Because of its high sensitivity and high temporal resolution, its capability to resolve multi-exponential decay functions, and its easy combination with laser scanner ophthalmoscopy, multi-dimensional time-correlated single photon counting was used for fundus imaging. An optimized set up for in vivo lifetime measurements at the human eye-ground will be explained. In this, the fundus fluorescence is excited at 446 or 468 nm and the time-resolved autofluorescence is detected in two spectral ranges between 510 and 560 nm as well as between 560 and 700 nm simultaneously. Exciting the fundus at 446 nm, several fluorescence maxima of lifetime t1 were detected between 100 and 220 ps in lifetime histograms of 40 degrees fundus images. In contrast, excitation at 468 nm results in a single maximum of lifetime t1 = 190 +/- 16 ps. Several fundus layers contribute to the fluorescence intensity in the short-wave emission range 510-560 nm. In contrast, the fluorescence intensity in the long-wave emission range between 560 and 700 nm is dominated by the fluorescence of lipofuscin in the retinal pigment epithelium. Comparing the lateral distribution of parameters of a tri-exponential model function in lifetime images of the fundus with the layered anatomical fundus structure, the shortest component (t1 = 190 ps) originates from the retinal pigment epithelium and the second lifetime (t2 = 1,000 ps) from the neural retina. The lifetime t3 approximately 5.5 ns might be influenced by the long decay of the fluorescence in the crystalline lens. In vitro analysis of the spectral properties of expected fluorophores under the condition of the living eye lightens the interpretation of in vivo measurements. Taking into account the transmission of the ocular media, the excitation of NADH is unlikely at the fundus.