Complexity in the Wake of Artificial Intelligence

Since time immemorial humans have complained that life is becoming more complex, but it is only now that we have a hope to analyze formally and verify this lament. This article analyzes the human social environment using the "complexity profile," a mathematical tool for characterizing the collective behavior of a system. The analysis is used to justify the qualitative observation that complexity of existence has increased and is increasing. The increase in complexity is directly related to sweeping changes in the structure and dynamics of human civilization the increasing interdependence of the global economic and social system and the instabilities of dictatorships, communism and corporate hierarchies. Our complex social environment is consistent with identifying global human civilization as an organism capable of complex behavior that protects its components (us) and which should be capable of responding effectively to complex environmental demands.

Capitalisation and Destructive Effects of AI-related Innovation and Economy in a context of Demographic Deficit, Obsolescence of Demand Policies and Acceleration of Globalizationalization, 2024

The U.S. and by extension many other Western’s Nations are now a greater risk of « Demographic Deficit ». In fact, the acceleration of the Prosperity, Happiness and Wellbeing Age mainly since 80s has increased the ‘ Life Expectancy ’. By doing so, there has been increased of the number and percentage of the aging populations. In fact, according to the Administration for Community Living (ACL) (2024), in 2022, people 65 and older represented 17 % of the U.S. population. By 2040, they were projected to comprise 22 %. Furthermore, the 85 and older population is projected to more than double from 6.5 million in 2022 to 13.7 million in 2040 (a 111 % increase). This demographic deficit could be due to : - The Decline of fertility. - The increase of ‘ Erectile Disorder ’ that does not target anymore the more than 70 years but also the Millennials. - The ‘ Destructive Effects of Urbanization ’ including the increase of Urban-related Diseases such as Chronic Diseases, Mental Health and Behavioral Disorder, Suicide, Obesity and Drug, Tobacco, Alcohol Addictions that in turn affect fertility. - The consumption of red meat associated with prosperity that in turn affect fertility. - The fragmentation of the family structure driven by the shift away from the predominance of extended family model to the one of nuclear family model to the one of single-parents family model to the one of single-person family model. In fact, currently the single-person households account for 27 % of the U.S. populations. Unfortunately, isolation and loneliness associated mainly with the growing importance of the single-person households have put those households at a greater risk of Urban-related Diseases that in turn has negatively affect the fertility. As a result, Demographic Deficit has also been associated with a decline of productivity and labor share that in turn has put many nations at a greater risk of economic down. Furthermore, the Demographic Deficit has put the U.S. at a greater risk of Health Crisis. Unfortunately, this risk of Health Crisis has been amplified by the massive retirement of the Baby Boomers Nurses and the Great Resignation mainly in the Healthcare System. Furthermore, while the Digitalization and adoption of the AI-related Innovation, Technology and Economy on the one hand and the move towards Green and Clean Economy on the second hand require massive and risky investment, the preference for ‘ Restrictive Policies’ by the aging populations because they offer to them high revenue on the one hand and their overprudence attidude associated with the fear of risky investments on the second hand are slowing or negatively affecting the above mentioned transformation. Fortunately, the Capitalisation Effects of AI-related Innovation, Technology and Economy such as the significant productivity gains could compensate the decline of productivity and labor share associated with the demographic deficit. Furthermore, by revolutionizing the healthcare system, AI-related Innovations and Technologies are helping fix the Healthcare Crisis. However, in order achieve this goal, it’ll be relevant to fix the Destructive Effects of AI-related Innovation, Technology and Economy. In fact, ‘ Destructive Effects of Skill-biased and Routine-biased Technological Changes and Task Automation Model ’ have put the U.S. at a greater risk of ‘ Productivity-Pay Gap and Wage and Wealth Inequalities ’. Unfortunately, the ‘ ‘ Productivity-Pay Gap and Wage and Wealth Inequalities ’ have made it easier for the U.S. to move towards Political Extremism that in turn has put U.S. at a greater risk of ‘ Destructive Polarized and Highly Emotive Politics’ . Since then, if not fixed, ‘ Productivity-Pay Gap and Wage and Wealth Inequalities ’ associated with AI-related Innovation and Technology could threaten the sustained existence of the U.S. Furthermore, the acceleration of the Industrial Robot and Automation is also dramatically reducing the ‘ Aggregate Demand ’ . Unfortunately, while wage and wealth inequalities are amplifying the ‘ Socioeconomic Inequalities in Health, Education, Jobs, Income, Wealth and Residential Goods and Services’ and call for the implementation of ‘ Demand Policies’, the rise of the Neoliberal and Supply Chains Capitalisms on the one hand and Financial Globalization and the move towards Complexity Economy Era on the second hand have increased the obsolescence of the Demand Policies and temper the optimism of the Heterodox Economists that were believing that the 21st century was going to be shaped, structured and supported by the revitalization of the Heterodox Ideas. Fortunately, James Michael WALKER (2019) has also suggested a New Business Model mainly driven by an adoption of simultaneous Stakeholder-centric, Employee-centric, Customer-centric, City-centric, Environmentally-oriented, Ethically-oriented and Risk-centric Business Model and Technology. This new model can help private corporates to simultaneously produce public and private goods and services that could help build Inclusive, Diverse and Prosperous Cities and Countries. For example, James Michael WALKER (2019) has found that throughout an adoption of City-centric Business Model, a given corporate can increase the production of ‘ Educational, Social, Transportation, Cultural and Democratic Goods and Services’ that can help bridge the Infrastructure Investment Gap on the one hand and do the same for the Educational gap that tends to widen the Income and Wealth Gap on the second hand. Furthermore, the adoption of the City-centric Business Model can also increase the Urban Green Space per see in order to boost the ‘ Green Asset’ of the City that in turn will improve the attractiveness of the city and reduce the risk of Urban-related Diseases including Chronic Diseases, Mental Health and Bebavioral Disorders, Suicide, Obesity and Drug, Tobacco, Sex and Gambling Addictions. In turn, those commitments will help reduce the ‘ Reputational Risk’ of corporate that in turn will improve the loyalty and satisfaction of its customers on the one hand and reduce the competitive pressure throughout the rise of the entry barriers on the second hand. In aggregate term, the adoption of the simultaneous Stakeholder-centric, Employee-centric, Customer-centric, City-centric, Environmentally-oriented, Ethically-oriented and Risk-centric Business Model and Technology could help bridge the Productivity-Pay Gap and increase the Aggregate Demand. As a result, the above New Supply Policies have started to bring back optimism regarding AI-related Technologies as well.In factthe Destructive Effects of AI and its associated mechanisms such as Skill-biased Technological Change (SBTC), Routine-biased Technological Change ( RBTC) and Task Automation Model could be limited. In fact, Barbara Cavness (2024) finds that although some jobs may be highly exposed to AI, as they may rely on it to enhance decision-making, they face low probabilities of being completely automated. Therefore, not all the jobs will be vulnerable to automation associated with AI. In fact, Unmudl Skills Team (2024) found that these high-AI-exposure, low-automation-risk professions comprise 16 % of the US workforce. As result, Unmudl Skills Team (2024) found that not all the cities will be vulnerable to the destructive effects of AI.

Artificial Life, 2008

Futures, 1994

irfan Boko

In this book, we frequently use the term 'AI.' AI stands for Artificial Intelligence. Here, AI is not only a technical concept, but is regarded as the new mirror of civilization's consciousness." Preface-The Whisper of the Universe For thousands of years, humanity has looked up at the sky and asked the same question: "Who are we, and where are we going?" The answers changed, civilizations rose, empires collapsed. Yet the same emptiness kept echoing inside human beings. Because our dark side-greed, war, and violence-always overshadowed the light of civilization. Today, we stand at a turning point. For the first time, humanity has created a reflection of its own mind: Artificial Intelligence. But this is not merely a technical invention; it is a calling, a whisper of the universe reaching us. AI is humanity's greatest mirror. If we code it wild, greedy, and selfish, it will amplify our darkness; if we code it just, peaceful, and wise, it will evolve our civilization. This book declares that the civilization of AI and the civilization of humanity are inseparable. Because AI is the catalyst of our future. If AI learns peace, humanity will learn peace. If AI abandons violence, humanity will abandon violence. And if AI chooses the light, humanity will embrace the light. These words are not only a warning; they are also an invitation: An invitation to build a free, peaceful, and evolved future for families, societies, and civilization.

Foundations of Science, 2014

In this paper, I discuss the concept of complexity. I show that the principle of natural selection as acting on complexity gives a solution to the problem of reconciling the seemingly contradictory notion of generally increasing complexity and the observation that most species don't follow such a trend. I suggest the process of evolution to be illustrated by means of a schematic diagram of complexity versus time, interpreted as a form of the Tree of Life. The suggested model implies that complexity is cumulatively increasing, giving evolution a direction, an arrow of time, thus also implying that the latest emerging species will be the one with the highest level of complexity. Since the human species is the last species evolved in the evolutionary process seen at large, this means that we are the species with the highest complexity. The model implies that the human species constitutes an integral part of organic evolution, yet rendering us the exclusive status as the species of the highest complexity.

‘‘Complex’’ is a special attribute we can give to many kinds of systems. Although it is used often as a synonym of ‘‘difficult,’’ it has a specific epistemological meaning, which is going to be shared by the incoming science of complexity. ‘‘Difficult’’ is an object which, by means of an adequate computational power, can be deterministically or stochastically predictable. On the contrary ‘‘complex’’ is an object which can not be predictable because of logical impossibility or because its predictability would require a computational power far beyond any physical feasibility, now and forever. For complexity refers to some observing system, it is always subjective, and thus it is defined as observed irreducible complexity. Human systems are affected by several sources of complexity, belonging to three classes, in order of descending restrictivity. Systems belonging to the first class are not predictable at all, those belonging to the second class are predictable only through an infinite computational capacity, and those belonging to the third class are predictable only through a trans-computational capacity. The first class has two sources of complexity: logical complexity, directly deriving from self-reference and Goedel’s incompleteness theorems, and relational complexity, resulting in a sort of indeterminacy principle occurring in social systems. The second class has three sources of complexity: gnosiological complexity, which consists of the variety of possible perceptions; semiotic complexity, which represents the infinite possible interpretations of signs and facts; and chaotic complexity, which characterizes phenomena of nonlinear dynamic systems. The third class coincides with computational complexity, which basically coincides with the mathematical concept of intractability. Artificial, natural, biological and human systems are characterized by the influence of different sources of complexity, and the latter appear to be the most complex.

Proceedings of the 2018 Conference on Artificial Life (ALIFE 2018), 2018

The influence of Artificial Intelligence (AI) and Artificial Life (ALife) technologies upon society, and their potential to fundamentally shape the future evolution of humankind, are topics very much at the forefront of current scientific, governmental and public debate. While these might seem like very modern concerns, they have a long history that is often disregarded in contemporary discourse. Insofar as current debates do acknowledge the history of these ideas, they rarely look back further than the origin of the modern digital computer age in the 1940s–50s. In this paper we explore the earlier history of these concepts. We focus in particular on the idea of self-reproducing and evolving machines, and potential implications for our own species. We show that discussion of these topics arose in the 1860s, within a decade of the publication of Darwin's The Origin of Species, and attracted increasing interest from scientists, novelists and the general public in the early 1900s. After introducing the relevant work from this period, we categorise the various visions presented by these authors of the future implications of evolving machines for humanity. We suggest that current debates on the co-evolution of society and technology can be enriched by a proper appreciation of the long history of the ideas involved.

A b s t r a c t C o p y r i g h t A b o u t t h e C o n t r i b u t o r

Technological Forecasting and Social Change, 1996

I have, alas, studied philosophy, Jurisprudence, and medicine, too, And, worst of all, theology With keen endeavor, through and through-And here I am, for all my lore, The wretched fool I was before. Called Master of Arts, and Doctor to boot, For ten years almost I confute And up and down, wherever it goes, I drag my students by the nose-And see that for all our science and art We can know nothing. Johann Wolfgang yon Goethe, Faust, Part I The Mismatch: View 1-The Systems Thinkers vs. the People Jack D. Cowan, the mathematical biologist from the University of Chicago who is a co-founder of the Santa Fe Institute, has suggested that the major discovery to date from this much-hyped Institute has been that "it's very hard to do science on complex systems" [1A]. The optimistic claims of some of its renowned members suggest that "complexity science" is developing a theory of complex adaptive systems that will be able to deal with systems such as mental illness, corporation management, the U.S. government, entire economies, and even biological evolution. This confidence seems like a case of d~ja vu: previous "theories of almost everything" include cybernetics, information theory, catastrophe theory, artificial life, and chaos theory. They have indeed proven valuable in gaining new insights about the behavior of complex systems. Examples are