Algorithmic Society

A better understanding of our actions within the context of their proximal and distal causes will enable us to improve health by selectively enhancing factors that promote it and intervening, when appropriate, for those that detract from it. The accompanying view of individuals and collectives at higher levels of temporal and spatial scale this encourages will tend toward an antireductive dissolution of dualisms, like mind/body, nature/artifice, and individual/social. These are a few of the results we can anticipate to see from the relatively new field of computational social science, which is becoming a principle paradigm through which society and culture, and even social contracts, are being viewed.

In "The Rise of the Social Algorithm" David Lazer writes "Humanity is in the early stages of the rise of social algorithms: programs that size us up, evaluate what we want, and provide a customized experience. This quiet but epic paradigm shift is fraught with social and policy implications. ...The fact that human lives are regulated by code is hardly a new phenomenon. Organizations run on their own algorithms, called standard operating procedures. And anyone who has been told that “it’s a rule” knows that social rules can be as automatic and thoughtless as any algorithm. Our friends generally are a lot like us and news media have always had to choose to pay attention to some stories and not others, in part based on financial and cultural imperatives. Social and organizational codes have always resulted in filter bubbles. However, every system of rules and every filtering process has potentially quite different dynamics and normative implications. Therein lies the most important lesson of Bakshy et al.’s report: the need to create a new field around the social algorithm, which examines the interplay of social and computational code."

It's been well established that our early capacities for learning from others enabled culture-driven genetic evolution. To paraphrase Joseph Henrich, genetic evolution is shaping us to be cultural learners, and cultural evolution shapes our genetic evolution. But Pablo Reyes Arellano digs deeper when he states: "The process of cultural evolution occurs through an algorithm." Culture-driven genetic evolution can be described as social "algorithm-driven" genetic evolution. Our many algorithmic innovations (such as fire, cooking, water containers, plant knowledge, and projectile weapons) drove our genetic evolution, altering our physiology, and psychology. These shared algorithms were the earliest sort of intelligence outside our own minds that we created. On a daily basis we interact with them, are affected by them, and can even be destroyed by them. Algorithms have been with us long before our recognition of contemporary artificial intelligence. 

Yuval Harari famously claimed "organisms are algorithms.” He was close. What he probably should've said was "cultures are algorithms," which is a far more consequential statement anyway. The embodiment problem (substrate dependence) makes algorithmic biology very difficult, but algorithmic descriptions of behavior and certain taxonomic characters (insofar as they are superorganic) are common. The field of physics is based on this premise. Animal behaviorists can describe the behavior of single-celled organisms algorithmically. Can we do the same with a mouse? Or how about a human within the sociocultural context? Regardless of our actual ability or stated beliefs, this is the operating assumption within society, where algorithm-driven evolution is happening today. Consider that algorithms mediate many of our relationships with each other. They used for social media, online dating, college admissions, credit scores, and stock trading, to name just a few examples. Over time, these experiences create a social, cultural, and ontological shift.

"Every reform deliberately instituted in the structure of society changes both history and the selective forces that affect evolution - though evolutionary change may be the farthest thing from our minds as reformers. We are not free to avoid producing evolution: we are only free to close our eyes to what we are doing." - Garrett Hardin, 1971

Arellano asks: "Are the systems with which we have approached the management of problems in the past enough to deal with the complex environments we find ourselves in today?" An algorithmic society wouldn't need to be comprehensive, in any sense of the word, to have a profound influence. Ted Striphas echoed this in an article about "algorithmic culture" noting that "Today, culture may only be as good as its algorithms." There is both potential and danger. Jack Balkin writes: "We are moving beyond the Information Age to the Algorithmic Society, where new technologies permit both public and private organizations to govern large populations. This could lead to asymmetries of information, monitoring capacity, and computational power."

When ideas can rapidly recombine with other ideas, algorithmic evolution moves faster and we produce more innovations, and potentially better adaptive bodies of knowledge. Hence, larger and more interconnected societies tend to produce faster algorithmic evolution. Harari again: "Computer scientists have been learning how to create better and better electronic algorithms. [This] will create a tsunami that will wash everything in its way. But technology’s not deterministic. There is no determinism about where this idea would lead us in the coming decades, so we should aim not just to regulate but to somehow guide this tsunami in a better and wiser direction." Merged with Joseph Henrich's expansive description of the significance of culture, the question is: what do social algorithms mean for ethics, healthcare, energy, transportation, heavy industry, agriculture, and materials? What do they mean for environmental and sustainability problems, and our cultural evolution? Who is helping to guide the direction of our coevolutionary process to ensure our Algorithmic Society is responsive to the biological health of the planet? It takes visionaries, like Rachel Armstrong who can see a beneficial fusion of nature and machine, and Lewis, Arista, Pechawis and Kite, who bring Indigenous epistemologies and open new lines of discussion.

There are of course numerous caveats. Some of which have been raised by Ian Bogost and George Zarkadakis. What are the limits to our ability to understand ourselves and our society through the lens of algorithms? To what extent can intuition be said to be algorithmic? And a real human society is so complex that, even assuming we can describe it algorithmically, things may be interconnected in a different way than we think they are. But that said, some rough contours are apparent, and there are many other very real features and behaviors that we can effectively describe as the product of individual and collective algorithmic minds and cultures, with useful results. If we can predict which policies will produce the best outcomes, for example, maybe we’ll end up with a healthier and happier world.

Consider that algorithmic modeling has shown that people tend to secularize when four factors are present: existential security, personal freedom, pluralism, and education. If even one is absent, the whole secularization process slows down. (Incidentally, this likely explains why the U.S. is secularizing at a slower rate than Western and Northern Europe.) This introduces the potential for social engineering, which hasn't escaped the attention of various groups like Cambridge Analytica. Bad actors will do this kind of computational work without transparency or public accountability. LeRon Shults points out “It’s going to be done. So not doing it is not the answer.” Instead, he believes the answer is to do the work with transparency and simultaneously speak out about the ethical danger inherent in it.

There is likely a collection of algorithms that would be able to describe my behavior to an arbitrary level of accuracy (a valuable commodity that is bought and traded by organizations that sell our data). The question is whether, and in what ways, this data can benefit us. Social algorithms should be studied. To reinterpret a well known quote from Jung: "We need more understanding of human algorithms. Our psyche should be studied..." It begins with knowing your personal data and algorithms, and those you share collectively with others. Who is doing this sort of work today? Iyad Rahwan leads the Scalable Cooperation group at the MIT Media Lab. His recent work has been on the topic of algorithmic social contracts. This dovetails into the research neuroscientist Molly Crockett has done on bridging the empathy gap, which is also partially motivated with the goal of creating better social contracts, and the research Peter Corning has done into the topics of society, synergy, and evolutionary transitions. As Rahwan writes: "We need to build new tools to enable society to program, debug, and monitor the algorithmic social contract between humans and governance algorithms."

Elinor Ostrom described how people can manage common-pool resources for centuries with little or no over exploitation. Notably, the answer could not be provided by formal models, but required detailed documentation from history and ethnography. J. D. Trout, in his book The Empathy Gap, wrote that our efforts to improve well-being must be more deliberate, surgical, and fully informed by the subtleties of scientific findings. In case it's not clear, "policy by intuition and and impressionistic judgment has had its run." Alex Pentland said theorists like Adam Smith and Karl Marx only had half the answers. His study of social physics, which he calls "Promethean fire," marks a qualitative change that is taking place in our understanding of human interaction. Managing the commons, bridging the empathy gap, and leveraging social networks are all fundamentally complex global challenges that only become tractable once we begin to look at the details, and get a fine grain understanding of individual interactions. As Vyacheslav Polonski writes, "Computational social scientists can truly make a difference, particularly by engaging with other actors from public policy and strategy in collaborative work, integrating analysis, intervention and implementation."

Key terms: algorithmic society, computational social science, social physics, ethical calculus, ethical algorithm, felicific calculus, stigmergic algorithm, empathy gap, cultural evolution, collective brain, complex systems, memetics

Additional sourced information:
"What potential value might a computational social science, based in an open academic environment, offer society, through an enhanced understanding of individuals and collectives?"
        - Life in the network: the coming age of computational social science (2009)

"Social science is about how people think (psychology), handle wealth (economics), relate to each other (sociology), govern themselves (political science), and create culture (anthropology)... The complex triadic nexus among social, artefactual, and natural systems requires computational investigation within an overall science of complexity."
"Computational social scientists strongly believe that a new era has started in the understanding of the structure and function of our society at different levels. ...characterized by new data at higher levels of temporal and spatial scale, and by new principles and concepts."
"Computational social science is aimed to pay attention to the entities the social world consists primarily of, i.e., people, ideas, human-made artefacts, and their relations within ecosystems. These entities are modelled as computational objects that encapsulate attributes and dynamics ...to both explain phenomena of interest and predict their evolution."
"The role of computational social science is a leading one in addressing the Big Problems of society, avoiding crises and threats to its stability and healthy development... According to the results of the Harvard Symposium on hard social problems in 2010, one of the top-ten problems is how to achieve good collective behaviour... Many bad environmental practices arise when citizens do not coordinate in order to attain a global optimal usage of collective resources, but rather pursue their own profit selfishly – resulting in an even worse long term individual performance."
"The development of Computational Social Science will make it possible to model and simulate social processes on a global scale, allowing us to take full account of the long distance interdependencies that characterise today’s heavily interconnected world. The output of these simulations will be used to support policy makers in their decision making, to enable them to efficiently and effectively identify optimal paths for our society. Similarly, open access to these large scale simulations will support individuals in their evaluation of different policy options in the light of their personal needs and goals, greatly enhancing citizen participation in this decision process. These developments together open the doors to a much safer, more sustainable and fairer global society."
         - Manifesto of computational social science (2012)

"This is the first time in human history that we have the ability to see enough about ourselves that we can hope to actually build social systems that work qualitatively better than the systems we've always had. That's a remarkable change. It's like the phase transition that happened when writing was developed."
"We can potentially design companies, organizations, and societies that are more fair, stable and efficient as we get to really understand human physics at this fine-grain scale. This new computational social science offers incredible possibilities."
        - Alex Pentland (2012)

"Human behavior is determined as much by the patterns of our culture as by rational, individual thinking. These patterns can be described mathematically, and used to make accurate predictions. The new science of “social physics” can build a predictive, computational theory of human behavior, a data-driven operating system for humanity.
"This is the first time in human history that we have the ability to see enough about ourselves that we can hope to actually build social systems that work qualitatively better, more fair, stable and efficient, than the systems we've always had. That's a remarkable change. It's like the phase transition that happened when writing was developed or when education became ubiquitous, or perhaps when people began being tied together via the Internet. This new computational social science offers incredible possibilities.
"A nation with poor measures of justice or inequality normally also has higher levels of corruption, and a nation with a poor record in poverty or sustainability normally also has a poor record of economic stability. Never again should it be possible to say “we didn’t know.” No one should be invisible. This is the world we want—a world that counts.
"If we're going to reinvent what it means to have a human society, one of the questions is: who is this new world going to be for, and what is it going to look like? There's a real focus on building a sustainable future, which means one in which there aren't large chunks of the population left out in the cold.
"You can begin to build a world where infectious pandemics cease to be as much of a threat. Similarly, if you're worried about global warming, you can design cities that are far more efficient, far more human, and burn an awful lot less energy. You could engineer transportation, energy, and health systems that would be dramatically better. We can get down and design things that really work for us on a personal level, rather than just being treated as another consumer.
"But you need to be able to see the people moving around in order to be able to get these results. If you could see everybody in the world all the time, where they were, what they were doing, then you could create an entirely different world. It's everybody contributing his or her data that's going to make a greener world. We need to stop pandemics. We need to make a greener world. We need to make a fairer world.
"There are some elements of this new data driven world that are really promising. For instance, the most efficient and robust architectures tend to be ones that have no central points. It means that there's no single place for a dictator to grab control. Also there is inherent in a society built on data sharing a certain level of transparency and choice for individuals that I believe will tend to mitigate against central control. It tends to dissolve the power of the state and big organizations because you can build things that are far more efficient and robust if they're distributed.
"Today societies' systems are built on big averages and indices, and the ideas of Adam Smith and Karl Marx, but these theorists only had half the answers. They talked about markets and classes, but those are only averages. Social phenomena are really made up of millions of small transactions between individuals. We're at a phase transition, moving from the reasoning of the enlightenment about classes and about markets to a fine grain understanding of individual interactions and systems built on fine grain data sharing. The patterns in those individual transactions are not just averages, but things that are responsible for the flash crash and the Arab spring. You need to get down into these new patterns, these micro-patterns, because they don't just average out to the classical way of understanding society. We're entering a new era of social physics, where it's the details of all the particles — the you and me — that actually determine the outcome. This new capability of looking at the details, and getting to really understand human physics at a fine-grain scale will give us the other half of the story.
"It is the connections between people that are at the core of making systems work well. The challenge is to figure out how to analyze the connections and come to a new way of building systems based on understanding these connections, the causality of connections in the real world.
"George Orwell was not nearly creative enough when he wrote 1984. We can use our understanding of the way things work to screw ourselves up really badly, or we can use it to build a better society. This is Promethean fire.
        - Alex Pentland, Reinventing Society in the Wake of Big Data

"Just as the goal of traditional physics is to understand how the flow of energy translates into changes in motion, social physics seeks to understand how the flow of ideas and information translates into changes in behavior... how people cooperate to discover, select, and learn strategies and coordinate their actions." (5, 16)
"Sustaining a healthy, safe, and efficient society is a scientific and engineering challenge that goes back to the 1800s, when the Industrial Revolution spurred rapid urban growth and created huge social and environmental problems... We have cities jammed with traffic, worldwide outbreaks of diseases, and political institutions that are deadlocked and unable to act. In addition, we face the challenges of global warming; uncertain energy, water, and food supplies... But it doesn't have to be this way. We can have cities that are energy efficient, have secure food and water supplies, and much better government. To reach these goals, however, we need to radically rethink our approaches. Rather than static systems that are separated by function - water, food, waste, transport, education, energy, and so on - we must consider them as dynamic and holistic systems." (138)
"I believe there are three design criteria for our emerging hypernetworked societies: social efficiency, operational efficiency, and resilience. Let us look at each of these in turn and then ask how they might apply to governments and society more generally." (203)
"The most important limitation is that we are attempting to infer causal processes from observational data in which many mechanisms are likely at play. If we find that behaviors between two individuals are correlated, it could be due to influence, but it could also be due to selection or to contextual factors. These mechanisms are generically confounded. However the ability to use time data to test causal ordering, as well as asymmetries in network relationships to test the direction of effects, provides greater confidence when inferring causality..." (249)
"The main point is that the propagation of human action habits by means of social learning can be accurately modeled from easily observable behavior using heterogenous, dynamic, stochastic networks. This capability is transformative for increasing our understanding of the dynamics of human society, and hence our ability to plan for the future." (264)
        - Alex Pentland, "Social Physics" (2014)

"Social Persuasion to influence the actions, beliefs and behaviors of individuals, embedded in a social network, has been widely studied. It has been applied to marketing, healthcare, sustainability, political campaigns and public policy. Traditionally, there has been a separation between physical (offline) and cyber (online) worlds. While persuasion methods in the physical world focused on strong interpersonal trust and design principles, persuasion methods in the online world were rich on data-driven analysis and algorithms. Recent trends including internet of things, `Big data', and smart phone adoption point to the blurring divide between the cyber and the physical worlds in the following ways. Fine grained data about each individual's location, situation, social ties, and actions are collected and merged from different sources. The messages for persuasion can be transmitted through both worlds at suitable times and places. The impact of persuasion on each individual is measurable. Hence, we posit that social persuasion will soon be able to span seamlessly across these worlds and will be able to employ computationally and empirically rigorous methods to understand and intervene in both cyber and physical worlds. Several early examples indicate that this will impact the fundamental facets of persuasion including who, how, where and when, and pave the way for multiple opportunities as well as research challenges."
        - Vivek Singh, Ankur Mani, and Alex (Sandy) Pentland, "Social Persuasion in Online and Physical Networks"

Postscript 1: In my earlier article about algorithms, I explored them primarily through the lens of the physical sciences. In this article, I wanted to take a closer look at them through the humanities and social sciences, to employ the simple but useful division within academia.

Postscript 2: A common misconception is that algorithms are just math. The word does vaguely sound like algebra or logarithm, doesn't it? But it's more useful to think of them as a set of rules that describe how to perform a task, or a flowchart that helps you arrive at a decision. Peter Kassan wrote a very nice article (not available online) titled: "I’ve Got Algorithm. Who Could Ask for Anything More?" He finds today's algorithms wanting in their ability to find causal relationships. The first part of his article provides several useful examples:

"Examples of early classical algorithms include Euclid’s Algorithm, which finds the greatest common divisor of two whole numbers; The Sieve of Eratosthenes, which finds prime numbers; and Binary Search, which finds an item in a sorted list. More prosaic examples are the procedures you learned in elementary school to do arithmetic — the ways you do addition, subtraction, multiplication, and division (if you still do them yourself) are all algorithms."

But as we tend to use the word today, "computer program" is in many cases a more appropriate term to algorithm. Whereas an algorithm (or heuristic) has an actual idea behind it, a computer program may or may not. However a bigger problem, and one which hasn't escaped the attention of researchers like Judea Pearl, is that most computer programs primarily look for correlations within data. If you haven't identified and gathered the metric that's actually causal, no amount of data will help. The fundamental goal of science is to find causal relationships — and this often involves inventing new instruments, tools, and materials to make new observations and measurements guided by new insights and new ideas." 

Source: "I’ve Got Algorithm. Who Could Ask for Anything More?" by Peter Kassan

Skeptical Inquirer, Sept/Oct 2018