Local measures of information storage in complex distributed computation

Self Cite

Abstract:Understanding epidemic dynamics has always been a challenge. As witnessed from the ongoing Zika or the seasonal Influenza epidemics, we still need to improve our analytical methods to better understand and control epidemics. While the emergence of complex sciences in the turn of the millennium have resulted in their implementation in modelling epidemics, there is still a need for improving our understanding of critical dynamics in epidemics. In this study, using agent-based modelling, we simulate a Susceptible-Infected-Susceptible (SIS) epidemic on a homogeneous network. We use transfer entropy and active information storage from information dynamics framework to characterise the critical transition in epidemiological models. Our study shows that both (bias-corrected) transfer entropy and active information storage maximise after the critical threshold (R 0 = 1). This is the first step toward an information dynamics approach to epidemics. Understanding the dynamics around the criticality in epidemiological models can provide us insights about emergent diseases and disease control.

Section: Information Dynamicsmentioning

confidence: 99%

Section: Information Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Criticality and Information Dynamics in Epidemiological Models

Erten

Lizier

Piraveenan

et al. 2017

Self Cite

“…For a 2-agent, 1-step Markovian game, the total information from agent i's previous state space to i's next act is encoded in the sum of agent i's Transfer Entropy from agent −i's previous act to agent i's current act and agent i's "active memory" [31] of their own past acts: I(S i n : a i n+1 ) = T a −i →a i + I(a i n : a i n+1 ).…”

Section: Corollarymentioning

confidence: 99%

Utility, Revealed Preferences Theory, and Strategic Ambiguity in Iterated Games

Harré

2017

Iterated games, in which the same economic interaction is repeatedly played between the same agents, are an important framework for understanding the effectiveness of strategic choices over time. To date, very little work has applied information theory to the information sets used by agents in order to decide what action to take next in such strategic situations. This article looks at the mutual information between previous game states and an agent's next action by introducing two new classes of games: "invertible games" and "cyclical games". By explicitly expanding out the mutual information between past states and the next action we show under what circumstances the explicit values of the utility are irrelevant for iterated games and this is then related to revealed preferences theory of classical economics. These information measures are then applied to the Traveler's Dilemma game and the Prisoner's Dilemma game, the Prisoner's Dilemma being invertible, to illustrate their use. In the Prisoner's Dilemma, a novel connection is made between the computational principles of logic gates and both the structure of games and the agents' decision strategies. This approach is applied to the cyclical game Matching Pennies to analyse the foundations of a behavioural ambiguity between two well studied strategies: "Tit-for-Tat" and "Win-Stay, Lose-Switch".

“…The higher the CE, the higher the complexity of the series is. CE and sE can be seen as portions of the decomposition of the overall amount of information carried by a series given that their sum is the Shannon entropy (ShE) [7][8][9][10]. While the CE is more widely utilized as a measure of complexity of a series [1][2][3], sE is traditionally exploited to assess regularity and predictability of a process [8] or information stored in it [7,9].…”

Section: Introductionmentioning

confidence: 99%

A Refined Multiscale Self-Entropy Approach for the Assessment of Cardiac Control Complexity: Application to Long QT Syndrome Type 1 Patients

Bari

Girardengo

Marchi

et al. 2015

Abstract:The study proposes the contemporaneous assessment of conditional entropy (CE) and self-entropy (sE), being the two terms of the Shannon entropy (ShE) decomposition, as a function of the time scale via refined multiscale CE (RMSCE) and sE (RMSsE) with the aim at gaining insight into cardiac control in long QT syndrome type 1 (LQT1) patients featuring the KCNQ1-A341V mutation. CE was estimated via the corrected CE (CCE) and sE as the difference between the ShE and CCE. RMSCE and OPEN ACCESSEntropy 2015, 17 7769 RMSsE were computed over the beat-to-beat series of heart period (HP) and QT interval derived from 24-hour Holter electrocardiographic recordings during daytime (DAY) and nighttime (NIGHT). LQT1 patients were subdivided into asymptomatic and symptomatic mutation carriers (AMCs and SMCs) according to the severity of symptoms and contrasted with non-mutation carriers (NMCs). We found that RMSCE and RMSsE carry non-redundant information, separate experimental conditions (i.e., DAY and NIGHT) within a given group and distinguish groups (i.e., NMC, AMC and SMC) assigned the experimental condition. Findings stress the importance of the joint evaluation of RMSCE and RMSsE over HP and QT variabilities to typify the state of the autonomic function and contribute to clarify differences between AMCs and SMCs.