The main aim of the book “ Ambiguity function in the process of reading the text authored by Forough Sahba” is to examine complexities discursivities, and narrative techniques making the stories ambiguous. Although the author attempts to present a host of evidence on ambiguous-making techniques for modern stories, there still exist some criticisms on the structure and content of the book which requires its examination more closely. Disorganization and weakness in the classification of the book’ s contents besides misreading of “ ambiguity” are the main drawbacks of the present book. More evidently, reducing the ambiguity in stories, merely modern and postmodern ones, equaling the ambiguity with its complexities and discursivities, and also downgrading the ambiguity which are perceived as strategies for maintaining the author’ s authority, are the main motives for writing this paper.

Rapid environmental changes and rapid technological advancements, along with wider communication and large volumes of information, have made organizations more complex than ever, and their management has become much more difficult. The purpose of this paper was to identify the most important types of organizational Complexity in the first stage and, secondly, to determine the ranking of organizations based on the Complexity of the three types of service, agriculture, and industry organiztions. The present study in terms of research methodology is quantitative and to analyze data the hierarchical analysis process (AHP) method has been used. The statistical population consists of 16 academic experts that also work at industry, service, or agriculture field. The results showed that the most important complexities in order of importance include: Environmental Complexity (491/0), Structural Complexity (201/0), Technological Complexity (169/0) and information Complexity (140/0). Also, the results showed that the most complex organizations are as follows: industrial organizations (421/0), service organizations (348/0) and agricultural organizations (231/0).

The scientific explanation is usually defined as finding the cause. For some reasons, this has been a subject of doubt, however, it seems that, both in common sense as well as in philosophical approach, the mainstream still support the very Idea of explanation something as introducing its cause. But cause searching and cause finding are not so easy tasks. This Article is for corroborating the idea according to which the essential tension in scientific research is derived from the practical difficulty of distinguishing a complex netting of causes, not from the theoretical disfunction of causality. Multiplicity, variety, interaction, and dynamism of causes and factors intervening in a causal process constitute oftentimes a perplexing netting in which it is difficult, sometimes practically impossible, to distinguish, dissociate, evaluate, and calculate vectors effective on final resultant and explain or predict the output or the effect the process in question has. Not paying enough attention to it leads mistakenly to doubt about causality itself. The problem is pragmatic, not paradigmatic.

Understanding the complex nature of the universe, the relationship between its components and processes has long been considered the major objective of the scientific discourses. Investigation of these relationships has been evolved and presented in the new scientific frameworks during the recent decades. This has led to the new explanation of the characteristics of universe. Although it has not been accompanied by the development of new scientific rules, the resulting approach has challenged the classical concept of physics and mathematics. Study of nonlinear and the dynamic systems led to the development of the chaos theory and eventually to Complexity in the last decades of the 20th century. This has helped us in better understanding of the laws of universe. The issue of Complexity through its close relation with the Chaos Theory has led to expanding the concepts and formulating them into the Complexity Theory framework. According to Complexity theory, sources of complex processes and behaviors of a system might be relying on the simple/repetitious actions of behaviors which might be turned into the complex phenomena in interaction with their environments. Ecological systems create the major components of our natural surrounding environment. They show complex patterns and behaviors in space and time. The knowledge of dominant processes and mechanisms of this environment and its components, therefore, can be viewed as an essential element for better understanding of the complex systems. Landscape is considered as an example of ecological systems and interacts with the larger environmental parameters and structures. This paper is aimed at the development of a framework within which the interaction of environment and complex system can be formulated and explained in accordance with landscape sustainability.

Complexity theory is the science of studying adaptive complex system. This science includes chaos theory, self-organization theory and fractal geometry. The origin of this theory is in Biology. Although there are common elements between ontology, epistemology and methodology of Complexity theory and postmodernism, we can't say Complexity theory is a postmodern theory because there are also similarities between Complexity science and positivism paradigm. In this paper, we will compare Complexity theory to postmodernism and positivism and the result is that Complexity theory is not fully a postmodern theory.

Purpose: Complexity and inflation are among the variables that consistently attract the attention of many policymakers and economists such that they are among the critical and effective issues in strategic and macroeconomic policies that can influence other economic variables. With a brief overview of the studies conducted on the economic Complexity index, one can find that, despite the numerous studies in this field, the role of economic Complexity in explaining the differences between developing and developed countries and its impact on the inflationary effect of liquidity, which is a key aim of macroeconomic policies, are somehow neglected. Considering the significance of economic Complexity and its potential influence on the inflation rate, the purpose of this study is to investigate the role of economic Complexity and its impact on inflationary effect of liquidity in 77 developing and developed countries. In order to answer why the Complexity of economies can reduce the intensity of inflationary effect of liquidity growth, the research model has been analyzed for 47 developing economies and 30 developed economies.Methodology: The research model to investigate the interactive effect of economic Complexity and liquidity on inflation is derived from the model proposed by Al Marhubi (2021), in which the logarithm of inflation is the dependent variable and a function of the annual change in the consumer price index. The research model is logarithmic. Liquidity is calculated as the difference between the logarithm of the liquidity volume and the logarithm of the GDP (at constant 2015 prices). The World Bank database was used to extract the data for these variables. The logarithm variable of economic Complexity measures the knowledge production capabilities of the economy and quantifies its capacity to generate complex goods. The Atlas of Economic Complexity is the source of the information contained in this index. In order to investigate the impact of economic Complexity on how liquidity affects inflation, the research model incorporates the interactive variable of the logarithm of economic Complexity and liquidity. Thus, it is expected that the interaction coefficient in the model will be negative. This implies that the intensity of the inflationary effect of liquidity will diminish as economic Complexity increases. Additional control variables, including the logarithm of per capita production (at constant 2015 prices) and the degree of the openness of the economy (sum of exports and imports divided by GDP as a percentage), were downloaded from the World Bank website. Also, the logarithm of the legal index of the central bank independence, which is referred to as the developed CBIE, is on a scale of 0 to 1, with 0 representing the lowest level of independence and 1 representing the highest level of independence. The data for this index was obtained from the Quality of Government (QoG) Institute in the Political Science Department of the University of Gothenburg. The average of five indices of electoral democracy, liberal democracy, participatory democracy, consultative democracy, and egalitarian democracy is used to calculate the logarithm of democracy. This index ranges from 0 (limited democracy) to 1 (high democracy). The data for this index were collected from V-DEM. The research period spanned from 1995 to 2021. The generalized method of moments (GMM) model was employed to conduct the research.Findings and Discussion: Based on the coefficients estimated through the GMM method, a rise in economic Complexity decreases the impact of liquidity on inflation. The coefficients of developing and developed countries are statistically significant and justifiable, and they are consistent with the research hypothesis. The coefficient of the interactive variable was -0.03 in developing countries and -0.05 in developed countries. As predicted by the research hypothesis, economic Complexity has a much more reducing impact on inflationary effect of liquidity in developed countries than in developing countries. In economies with a complex economic structure, liquidity growth is restricted, resulting in low and limited inflation. However, the ratio of liquidity to GDP is significantly higher than in economies with a low level of economic Complexity. The Sargan test was implemented to verify the coefficients of the model. As indicated by this test, the null hypothesis was rejected, and the estimated model proved to have the required validity. The significance of the total regression was also demonstrated by the Wald test result. In the initial instance, the null hypothesis was rejected by the Arellano-Bond test results. Therefore, the estimated coefficients were found valid.Conclusions and Policy Implications: The purpose of this research was to examine the role of economic Complexity in the relationship between liquidity and inflation. The research hypothesis was verified using the data from 77 countries from 1995 to 2021 and the GMM model. Also, the research hypothesis was evaluated for 47 developing countries and 30 developed countries. The research findings indicated that the impact of liquidity on inflation is diminished when the economic Complexity index is enhanced. In other words, when an economy has a high degree of Complexity, the increase in liquidity leads to lower inflation compared to an economy that has a lower degree of Complexity. The coefficient of the interactive variable was found to be -0.03 in developing countries and -0.05 in developed countries. As expected, based on the research hypothesis, in developed countries, economic Complexity has a much more reducing effect on the inflationary role of liquidity than in developing countries. Among the other research findings, one may refer to the negative impacts of the variables of per capita production, the degree of openness of the economy, the impact of the central bank independence and democracy on inflation, as well as the positive impact of liquidity on inflation in all research models. This study assists policymakers in comprehending the consequences of policies that are designed to enhance the country’s production capabilities and technical knowledge with respect to inflation, which is the primary objective of macroeconomic policies. Additionally, countries should specialize and diversify their export structures. The negative effect of economic Complexity on the inflationary effect of liquidity is in favor of development strategies that seek to diversify the export portfolio rather than specializing exports. This is because export diversification is a basic component of economic Complexity.