Information Flow in a Kinetic Ising Model Peaks in the Disordered Phase

Criticality and Information Dynamics in Epidemiological Models

“…This result aligns well with the peak in (collective) transfer entropy slightly in the super-critical regime in the Ising model [25]. In alignment with results in the Ising model, here once the disease dynamics reach criticality, we observe strong effects of one individual on a connected neighbour (measured by the transfer entropy).…”

“…Crucially, the peak of both these information-processing operations (measured with the bias correction) occurs at R 0 = 1.2, rather than the canonical R 0 = 1.0. As mentioned earlier, previous studies of distributed computation and its information-processing operations [23][24][25]27], concluded that the active information storage peaks just on the ordered side, while transfer entropy maximises on the disordered side of the critical threshold. Therefore, in our case, it may be argued that the concurrence of both bias-corrected peaks, as detected by the maximal information-processing "capacity" of the underlying computation, at R 0 = 1.2, indicates an upper bound for the critical threshold in the studied finite-size system.…”

“…For instance, in studies of the order-chaos phase transitions in random Boolean networks (driven by changes in activity [23] and/or network structure [24]), information storage has been shown to peak just on the ordered side, whereas information transfer was found to maximise on the other (chaotic) side of the critical threshold. Information transfer, from a collection of sources, has also been measured to peak prior to the phase transition in the Ising model, when the critical point is approached from the disordered (high-temperature) side [25]. And similarly, both information storage and transfer were measured to be maximised near the critical state in echo-state networks (a type of recurrent neural network) [26], where such networks are argued to be best placed for general-purpose computation.…”

Iterated Economic Games, Logic Gates, and the Flow of Shared Information in Strategic Interactions

“…Herding behaviour and financial contagion have been suggested as a possible mechanism that drives financial market collapses [40,41] and so it is important both theoretically and empirically to understand the limits of our ability to detect these two different behaviours. Previously, information theory has been used to measure abrupt transitions in general [20,42] and in financial market collapses specifically [43,44] but little progress has been made in relating information sets and strategic computation in economic theory, particularly as it relates to fundamentalists versus herders [45].…”

“…This is one of many different specifications of TE, generalisations based on history length appears in Schreiber's original article [18], and further considerations of delays [19] as well as averaging the TE over whole systems of stochastic variables [20] have also been developed, for a recent review see [21].…”