Risk-return relationship in a complex adaptive system

Analysing a Chinese Regional Integrated Healthcare Organisation Reform Failure using a Complex Adaptive System Approach

Introduction:

China’s organised health system has remained outdated for decades. Current health systems in many less market-oriented countries still adhere to traditional administrative-based directives and linear planning. Furthermore, they neglect the responsiveness and feedback of institutions and professionals, which often results in reform failure in integrated care. Complex adaptive system theory (CAS) provides a new perspective and methodology for analysing the health system and policy implementation.

Methods:

We observed the typical case of Qianjiang’s Integrated Health Organization Reform (IHO) for 2 years to analyse integrated care reforms using CAS theory. Via questionnaires and interviews, we observed 32 medical institutions and 344 professionals. We compared their cooperative behaviours from both organisational and inter-professional levels between 2013 and 2015, and further investigated potential reasons for why medical institutions and professionals did not form an effective IHO. We discovered how interested parties in the policy implementation process influenced reform outcome, and by theoretical induction, proposed a new semi-organised system and corresponding policy analysis flowchart that potentially suits the actual realisation of CAS.

Results:

The reform did not achieve its desired effect. The Qianjiang IHO was loosely integrated rather than closely integrated, and the cooperation levels between organisations and professionals were low. This disappointing result was due to low mutual trust among IHO members, with the main contributing factors being insufficient financial incentives and the lack of a common vision.

Discussion and Conclusions:

The traditional organised health system is old-fashioned. Rather than being completely organised or adaptive, the health system is currently more similar to a semi-organised system. Medical institutions and professionals operate in a middle ground between complete adherence to administrative orders from state-run health systems and completely adapting to the market. Thus, decision-making, implementation and analysis of health policies should also be updated according to this current standing. The simplest way to manage this new system is to abandon linear top-down orders and patiently wait for an explicit picture of IHO mechanisms to be revealed after complete and spontaneous negotiation between IHO allies is reached. In the meantime, bottom-up feedback from members should be paid attention to, and common benefits and fluid information flow should be prioritised in building a successful IHO.

THE "EVERYTHING'S DIFFERENT, EVERY TIME" INNOVATION MANAGEMENT PROBLEM: A PROMISING MODEL DEVELOPMENT

This study reports on the testing of a promising approach for aiding decision-making during innovation. By focusing on the effects of risk/action dyads on success (the Risk/Action/Success (R/A/S) framework), and because perceived risks do appear repeatedly even though they emanate from differing contexts, the model offers an opportunity to learn from what worked best before. Using Artificial Neural Networks, this novel approach allows for generalisation and applicability of specific innovation management actions that are context specific. For academics, the proposed approach contributes to the risk-management literature by proposing a new paradigm for understanding and analysing innovation processes and identification of the most frequently occurring risks as seen by managers directly involved in continuous innovation. In addition, the model offers the capacity to use quantitative techniques to model the overlapping risks and actions during innovation-related decision-making. For practitioners, it can provide specific recommendations in the form of success-sorted lists of actions taken by other innovation managers that faced similar risks. This paper presents the theoretical and practical rationales underpinning this R/A/S framework and reports on the viability of this approach using pilot data.

The leverage effect on wealth distribution in a controllable laboratory stock market

Wealth distribution has always been an important issue in our economic and social life, since it affects the harmony and stabilization of the society. Under the background of widely used financial tools to raise leverage these years, we studied the leverage effect on wealth distribution of a population in a controllable laboratory market in which we have conducted several human experiments, and drawn the conclusion that higher leverage leads to a higher Gini coefficient in the market. A higher Gini coefficient means the wealth distribution among a population becomes more unequal. This is a result of the ascending risk with growing leverage level in the market plus the diversified trading abilities and risk preference of the participants. This work sheds light on the effects of leverage and its related regulations, especially its impact on wealth distribution. It also shows the capability of the method of controllable laboratory markets which could be helpful in several fields of study such as economics, econophysics and sociology.

Dynamic evolution of cross-correlations in the Chinese stock market

The analysis of cross-correlations is extensively applied for the understanding of interconnections in stock markets and the portfolio risk estimation. Current studies of correlations in Chinese market mainly focus on the static correlations between return series, and this calls for an urgent need to investigate their dynamic correlations. Our study aims to reveal the dynamic evolution of cross-correlations in the Chinese stock market, and offer an exact interpretation for the evolution behavior. The correlation matrices constructed from the return series of 367 A-share stocks traded on the Shanghai Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a moving window with a size of 400 days. The evolutions of the statistical properties of the correlation coefficients, eigenvalues, and eigenvectors of the correlation matrices are carefully analyzed. We find that the stock correlations are significantly increased in the periods of two market crashes in 2001 and 2008, during which only five eigenvalues significantly deviate from the random correlation matrix, and the systemic risk is higher in these volatile periods than calm periods. By investigating the significant contributors of the deviating eigenvectors in different time periods, we observe a dynamic evolution behavior in business sectors such as IT, electronics, and real estate, which lead the rise (drop) before (after) the crashes. Our results provide new perspectives for the understanding of the dynamic evolution of cross-correlations in the Chines stock markets, and the result of risk estimation is valuable for the application of risk management.

A statistical physics view of pitch fluctuations in the classical music from Bach to Chopin: evidence for scaling

Because classical music has greatly affected our life and culture in its long history, it has attracted extensive attention from researchers to understand laws behind it. Based on statistical physics, here we use a different method to investigate classical music, namely, by analyzing cumulative distribution functions (CDFs) and autocorrelation functions of pitch fluctuations in compositions. We analyze 1,876 compositions of five representative classical music composers across 164 years from Bach, to Mozart, to Beethoven, to Mendelsohn, and to Chopin. We report that the biggest pitch fluctuations of a composer gradually increase as time evolves from Bach time to Mendelsohn/Chopin time. In particular, for the compositions of a composer, the positive and negative tails of a CDF of pitch fluctuations are distributed not only in power laws (with the scale-free property), but also in symmetry (namely, the probability of a treble following a bass and that of a bass following a treble are basically the same for each composer). The power-law exponent decreases as time elapses. Further, we also calculate the autocorrelation function of the pitch fluctuation. The autocorrelation function shows a power-law distribution for each composer. Especially, the power-law exponents vary with the composers, indicating their different levels of long-range correlation of notes. This work not only suggests a way to understand and develop music from a viewpoint of statistical physics, but also enriches the realm of traditional statistical physics by analyzing music.

An exotic long-term pattern in stock price dynamics

Background

To accurately predict the movement of stock prices is always of both academic importance and practical value. So far, a lot of research has been reported to help understand the behavior of stock prices. However, some of the existing theories tend to render us the belief that the time series of stock prices are unpredictable on a long-term timescale. The question arises whether the long-term predictability exists in stock price dynamics.

Methodology/Principal Findings

In this work, we analyze the price reversals in the US stock market and the Chinese stock market on the basis of a renormalization method. The price reversals are divided into two types: retracements (the downward trends after upward trends) and rebounds (the upward trends after downward trends), of which the intensities are described by dimensionless quantities, and , respectively. We reveal that for both mature and emerging markets, the distribution of either retracements or rebounds shows two characteristic values, 0.335 and 0.665, both of which are robust over the long term.

Conclusions/Significance

The methodology presented here provides a way to quantify the stock price reversals. Our findings strongly support the existence of the long-term predictability in stock price dynamics, and may offer a hint on how to predict the long-term movement of stock prices.

Contrarian behavior in a complex adaptive system

Contrarian behavior is a kind of self-organization in complex adaptive systems (CASs). Here we report the existence of a transition point in a model resource-allocation CAS with contrarian behavior by using human experiments, computer simulations, and theoretical analysis. The resource ratio and system predictability serve as the tuning parameter and order parameter, respectively. The transition point helps to reveal the positive or negative role of contrarian behavior. This finding is in contrast to the common belief that contrarian behavior always has a positive role in resource allocation, say, stabilizing resource allocation by shrinking the redundancy or the lack of resources. It is further shown that resource allocation can be optimized at the transition point by adding an appropriate size of contrarians. This work is also expected to be of value to some other fields ranging from management and social science to ecology and evolution.