Emergence—"a unifying theme for 21st century science”1—is 150 years old.2 It is the name given to new behaviors displayed by complex adaptive systems, specifically those that differ from the behaviors of the systems’ individual components.
Emergent behaviors can be expected or unexpected, and although beneficial unexpected emergence can be welcome, detrimental unexpected emergence is not. Given the increasing pace of global IT development, it is important to understand whether detrimental unexpected IT emergence is within organizations’ control.
Key Concepts in Emergence
Complex adaptive systems consist of components with their own evolving behaviors that interact with each other to create emergent behaviors.3 A system is defined as a set of components working together toward a common purpose.4 In information science terms, the complexity of a system is described by the minimum length of a description of a system, where the length of the description is measured in units of information.5 The more information needed to describe a system, the more complex it is. The more complex it is, the more likely it is to be unpredictable and to exhibit unexpected emergence.6
For example, a doorknob is a technology that integrates multiple parts to solve the problem of opening and closing a door. The growth and transmission of pathogens due to many people touching doorknobs—including some who may be ill and others whose personal hygiene may be lacking—is an unexpected emergence. The subsequent mitigation of this detrimental unexpected emergence comes from doorknobs made from or plated with copper, due to copper’s high performance as a bactericidal agent.7 A doorknob might be described by three units of information: a doorknob may be pulled to open a door, a doorknob may be pushed to close a door and a doorknob may be pushed down or twisted counterclockwise to unlatch a door. Compared to, say, a social media system consisting of hundreds of functional elements, this would make a doorknob a very simple system.
In mathematics, resultant vectors can be considered emergent. In a space consisting of two vectors (each representing the force on an object as its own behavior), the resultant vector behaves differently from the two individual vectors, both in quantity and direction (figure 1) and, thus, represents emergence.
Although the representation of a system in figure 1 is simple, predicting the resultant vector or equivalent—or indeed the behavior of new real-world systems of interacting components—is complex, because:
- Some of the components may not be identifiable or identified, such as E and F in figure 2. Making assumptions about them or not even knowing they are part of the system can amplify unexpected emergence.
- Individual components can themselves be systems rather than single components.
- All the component relationships are neither necessarily identifiable, identified nor understood, e.g. b, e, and f in figure 2.
- Some of the behaviors stemming from each individual component or relationship may not be understood or expected.
- The components are not necessarily limited to IT. They can include users, regulators, funders, skills, experiences, processes, procedures, governance and equipment, as well as access to power and access to telecommunications, sea, air, land, the electromagnetic spectrum and space.
A system can be considered as a whole or as the sum of its parts. Emergence occurs in a whole system, but not in its parts. Describing a system depends on the point of view of the observer. An observer looking at a whole system will see one set of behaviors, and an observer looking at parts will see the behaviors of the individual parts.
Holon is the noun for this dualism (figure 3), derived from the Greek “holos,” meaning all, and “on,” meaning part.8 The impact of not understanding all components and their behaviors is compromised understanding of the whole and, thus, of the holon. The visible orange section in figure 3 represents the gap in understanding if the system is only considered to contain the purple components, illustrating the potential for unexplained emergence.
The more complex the system, the more complex the emergence, and the less likelihood of awareness of all possible emergent behaviors under all possible operating scenarios. That some components can themselves be systems gives rise to a system of systems, dramatically increasing the complexity.
There are three types of emergence:9
- Simple—Also known as synergy, it refers to the components and relationships that occur in noncomplex (ordered) systems to create expected emergence, not necessarily in the absence of unexpected emergence.
- Weak—Anticipates and allows for expected emergence, but because the system is complex it cannot predict total emergence.
- Strong—Refers to unexpected emergence (e.g., in systems of systems) when two or more systems integrate to perform a common goal, possibly disruptively. Chaotic or unpredictable emergence is likely with systems of systems.
Emergence is difficult—sometimes impossible—to predict in a real-world system of individual components. Predictability is reasonable only with the lowest of type of emergence—simple emergence.
Note that emergence disguises cause and effect in a system; one cannot understand the system by analyzing the behavior of its individual components.10 This is referred to as irreducibility.
Examples of Detrimental Unexpected IT Emergence
There are many examples of detrimental unexpected IT emergence, whether from a data or an IT perspective. From a technology perspective, common examples are blockchain and social media, and from a data perspective, common examples come from aspects of data management.
Blockchain’s Ballooning Energy Needs
Technology emergence is related to the novelty, synergy and functionality of a system.11 For example, almost 30 years before the mythical Bitcoin creator Satoshi Nakamoto published his groundbreaking paper on Bitcoin in 2009, emergence was found to be paramount in distributed computing (a system such as Bitcoin runs off multiple networked computers rather than off a single computer). For the multiple networked computers to stay in sync, reaching what is known as consensus (achieved by proof of work in the case of the Bitcoin blockchain) was determined to be essential.12, 13 Proof of work is a mechanism that verifies a transaction before adding it to the blockchain. Blockchain functionality is the synergy of many individual components—such as encryption, open source software communities and distributed computing—that display distinct individual behaviors.
Emergence is difficult—sometimes impossible—to predict in a real-world system of individual components.
An unexpected emergent outcome of the Bitcoin blockchain is the huge energy need for proof of work. It consumes 127 terawatt hours (TWh) of electricity annually—as much as Ukraine or Sweden, which are among the top 30 energy-consuming countries in the world.14 It also produces 38 kilotons of electronic waste and has a carbon footprint of 71 million tons of carbon dioxide15—as much as Morocco or Romania, which are among the 60 countries with the highest carbon footprints.16
This behavior is different from the individual behaviors of the component systems (figure 4). The Ethereum blockchain (using the proof-of-stake validation mechanism rather than the more energy-intensive proof-of-work mechanism) requires significantly less electricity, just 0.01 TWh per transaction.17 Yet at 26.08 watt-hours (Wh) per transaction, Ethereum is still significantly more energy-intensive than Mastercard, for example, which requires 0.7 Wh per transaction.18
Sources: Digiconomist, "Bitcoin Energy Consumption Index,” https://digiconomist.net/bitcoin-energy-consumption and Digiconomist, "Ethereum Energy Consumption Index,” https://digiconomist.net/ethereum-energy-consumption
Social Media’s Human Toll
Another view of detrimental IT emergence is the acquisition and overuse of IT (including smartphones) by individuals, potentially resulting in detrimental physical health effects in adults and developmental impairments in children.19 One-third of the respondents to a Pew Research Center survey expected the detrimental impacts of technology to outweigh its beneficial impacts within the next decade.20
Social media has exposed young people to bullying, rumor-spreading and the pressure of unrealistic expectations.21 The detrimental impacts of IT on analytical thinking, memory, focus, creativity, reflection, mental resilience and dopamine addiction22 were unforeseen.
That social media technology contributed “more than minimally” to the death of a 14-year-old girl23 is among the worst forms of detrimental unexpected IT emergence. In the words of her grieving father, “it’s time to protect our innocent young people instead of allowing [social media] platforms to prioritize their profits by monetizing the misery of children.”24 This was not the first case of its type.
Impact of Individuals
In some cases, a single person’s entrepreneurial endeavors can be highly impactful. Consider Elon Musk, for example, the driving force behind Starlink,25 Tesla,26 and SpaceX,27 whose vision and influence have introduced unexpected emergence in a multitude of industries.
Examples of Detrimental Unexpected Data Emergence
Merging data sources with differing lineage can introduce beneficial and detrimental emergence resulting from:
- Timing differences between sources
- Differences in meaning between data elements
- The ways data are stored (e.g., data type and encoding), which, at first glance, may appear comparable but are actually completely different
Further examples of emergence include assumptions that data are accurate when they are not necessarily so, breaches of sensitive data when collected or stored, and uses of data in ways not initially imagined. Compounded emergence can occur in data management and data governance (domains that include privacy and security risk controls), for example, or in such new behaviors as the production and distribution of fake news and deep fakes.
Emergence is generally considered a final outcome, but it can occur at multiple points along the data value chain (figure 5). From a privacy perspective, at the input level, emergence may occur when third-party data are acquired without consent, as exposed during a privacy audit. At the processing level, emergence may occur in the form of data being processed in ways not initially agreed upon with the data source, as exposed during an EU General Data Protection Regulation (GDPR) audit.
Detrimental unexpected privacy and security emergence seems to come to a head in the breaches of the personal data that occur around the world. For example, a breach of the personal data of 9.8 million customers of Optus—a major Australian telecommunications organization that experienced the biggest data breach in Australia—shows that organizations “may be forced to dispose of what they don’t need, or fined more punitively if they misuse or lose what they have.”28 The Optus breach stressed the high cost of data hoarding.29
One reason it is taking so long for Australian organizations to become more aware of data risk is that Australia’s Privacy Commissioner is underfunded.30 This situation is not unique to Australia.31, 32 Furthermore, organizations have forgotten that customer data do not belong to them and that it is a privilege to be a data steward.33 Privacy and security need significant investments to stem declining trust in organizations.
Unexpected Detrimental IT and Data Emergence: Primary Risk
If outcomes cannot be predicted and a system cannot be understood, then realizing that the outcome can be detrimental for individuals, groups, organizations or even for society as a whole means that the variance in outcomes compared to expectations—risk—can be so significant that it can compromise an organization’s efforts to achieve its objectives or negatively impact society overall. Some factors driving the risk include:
- Unvalidated new technology
- Lack of consideration of cross-domain impact
- Disagreement about what the system is solving or providing
- Conflicts of interest about solutions to problems, such as between surveillance and privacy
- Increasing levels of connectivity between information technologies
- Contradictions and other differences between short-term and long-term values driving a solution34
- System unknowns, both known unknowns (for example, known gaps in a system’s capabilities for which no adequate response seems to exist) and unknown unknowns
- Increasing system performance expectations (E.g., tight coupling can expose weak links in high performance environments, whereas low performing components cannot be replaced without replacing entire sets of components.)
Since expected detrimental emergence should already have been considered by project risk management, it is specifically unexpected detrimental emergence that remains worrisome.
Although all emergence is a risk, it is detrimental emergence that causes the most trouble. However, since expected detrimental emergence should already have been considered by project risk management, it is specifically unexpected detrimental emergence that remains worrisome (figure 6).
Source: Adapted from Kopetz, H.; A. Bondavalli; F. Brancati; B. Frömel; O. Höftberger; S. Iacob; “Emergence in Cyber-Physical Systems-of-Systems (CPSoSs),” Cyber-Physical Systems-of-Systems (CPSoSs), 18 December 2016, https://link.springer.com/chapter/10.1007/978-3-319-47590-5_3#rightslink
A system (the holon) is defined not only by its technologies, but also by stakeholders, the solution space, enabling systems, interoperating systems (represented by E and F in figure 2 and figure 3), internal influences, and external influences including political, economic, social, technological, environmental and legal factors across the entire system’s life cycle.35 This is supported by a principle of systems engineering: “Decision quality depends on knowledge of the system, enabling system(s), and interoperating system(s) present in the decision-making process.”36 All this dramatically increases the scope of considerations required to understand the full risk profile of the holon.
Given this complexity, unexpected emergence or unintended consequences should actually be expected. The problem lies in not knowing what they might be or when they may present themselves.
If something as simple as a doorknob can produce detrimental outcomes, what about machines claiming to think as humans do or the maintenance of immutable records that requires increasing electric power for their sustenance?
Often missing in detrimental unexpected emergence is the acceptance of accountability for the outcomes experienced by the affected parties due to the actions of human progenitors—whether an individual or a group. If and when there is a response, there is also hope that the progenitor’s response to negative outcomes is ethical.
While enterprise risk management is always a factor in organizational IT, the risk of a technology to the social fabric that keeps people and communities connected as human beings is of greater concern. Think of the negative impacts of social media. It falls to human beings to identify technology-driven risk to the social fabric and to raise the alarm by any means possible. It cannot be expected that enterprises or organizations will do this given their potential or perceived conflicts of interest. Furthermore, regulators will be slow to respond while they wrestle with understanding the technology, and declining trust in government may compromise its ability to safeguard its citizens’ best interests.
Conclusion
The question is whether detrimental unexpected emergence in a holon is predictable—whether the risk can be identified, analyzed and at least partly controlled. The argument made is that detrimental unexpected emergence in holons is not necessarily identifiable, much less controllable, given the holon’s almost incalculable complexity, and that what is unknown is unknown.
This is, perhaps, a word of warning for the deployment of emerging systems such as blockchain and artificial intelligence (AI). Humans do not really know what is in store with these technologies. If something as simple as a doorknob can produce detrimental outcomes, what about machines claiming to think as humans do or the maintenance of immutable records that requires increasing electric power for their sustenance?
There is a great deal more uncertainty to come in the growing ecosystem of emerging technologies that break new ground in a particular field; converging technologies, which develop from different technologies with similar goals; and disruptive technologies, which displace earlier technologies.37
Therefore, more detrimental outcomes should be expected as society’s development of and dependence on IT grows. One way for humans to challenge the potential growth of detrimental outcomes is to highlight issues through the various channels available, such as by means of digital media and the municipal and regional channels of governments. If humans do not, the cost may, ultimately, be greater than what society has the capability to address.
Endnotes
1 Pines, D.; “Emergence: A Unifying Theme for 21st Century Science,” Santa Fe Institute, 31 October 2014, https://medium.com/sfi-30-foundations-frontiers/emergence-a-unifying-theme-for-21st-century-science-4324ac0f951e
2 Burmaoglu, S.; O. Sartenaer; A. Porter; “Conceptual Definition of Technology Emergence: A Long Journey From Philosophy of Science to Science Policy,” Technology in Society, vol. 59, November 2019, https://www.sciencedirect.com/science/article/abs/pii/S0160791X1930003X
3 Sullivan, T.; “Embracing Complexity,” Harvard Business Review, September 2011, https://hbr.org/2011/09/embracing-complexity
4 TechTarget, “System,” https://www.techtarget.com/searchwindowsserver/definition/system
5 New England Complex Systems Institute, “Concepts: Complexity,” https://necsi.edu/complexity
6 Adcock, R.; et al.; “Emergence,” Systems Engineering Body of Knowledge, https://www.sebokwiki.org/wiki/emergence
7 Colin, M.; et al.; “Copper Alloy Touch Surfaces in Healthcare Facilities: An Effective Solution to Prevent Bacterial Spreading,” National Library of Medicine, December 2018, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6317222/
8 Kopetz, H. et al.; “Emergence in Cyber-Physical Systems-of-Systems (CPSoSs),” Cyber-Physical Systems-of-Systems (CPSoSs), 18 December 2016, https://link.springer.com/chapter/10.1007/978-3-319-47590-5_3#rightslink
9 Op cit Adcock et al.
10 Op cit Sullivan
11 Op cit Burmaoglu et al.
12 Cucker, F.; S. Smale; “The Mathematics of Emergence,” The University of Utah, Salt Lake City, Utah, USA, 17 October 2006, http://www.cs.utah.edu/~tch/CS5300/resources/refs/Cucker2006.pdf
13 Tsitsiklis, J.; “Problems in Decentralized Decision Making and Computation,” Massachusetts Institute of Technology, Cambridge, Massachusetts, USA, November 1984, https://www.mit.edu/~jnt/Papers/PhD-84-jnt.pdf
14 World Population Review, “Energy Consumption by Country 2022,” https://worldpopulationreview.com/country-rankings/carbon-footprint-by-country
15 Digiconomist; “Bitcoin Energy Consumption Index,” https://digiconomist.net/bitcoin-energy-consumption
16 Op cit World Population Review
17 Digiconomist; “Ethereum Energy Consumption Index,” https://digiconomist.net/ethereum-energy-consumption
18 Ibid.
19 Johnson, J.; “Negative Effects of Technology: What to Know,” Medical News Today, 25 February 2020, https://www.medicalnewstoday.com/articles/negative-effects-of-technology
20 Anderson, J.; L. Rainie; “Concerns About the Future of People’s Well-Being,” Pew Research Center, 17 April 2018, https://www.pewresearch.org/internet/2018/04/17/concerns-about-the-future-of-peoples-well-being/
21 Mayo Clinic; “Tween and Teen Health,” https://www.mayoclinic.org/healthy-lifestyle/tween-and-teen-health/in-depth/teens-and-social-media-use/art-20474437
22 Op cit Anderson and Rainie
23 Crawford, A.; B. Bell; “Molly Russell Inquest: Father Makes Social Media Plea,” BBC News, 30 September 2022, https://www-bbc-co-uk.cdn.ampproject.org/c/s/www.bbc.co.uk/news/uk-england-london-63073489.amp
24 Ibid.
25 Clery, D.; “Starlink Already Threatens Optical Astronomy. Now, Radio Astronomers Are Worried,” Science, 9 October 2020, https://www.science.org/content/article/starlink-already-threatens-optical-astronomy-now-radio-astronomers-are-worried
26 Lin, P.; “Here’s How Tesla Solves a Self-Driving Crash Dilemma,” Forbes, 5 April 2017, https://www.forbes.com/sites/patricklin/2017/04/05/heres-how-tesla-solves-a-self-driving-crash-dilemma/
27 Skibba, R; “The FAA Weighs the SpaceX Launch Site’s Environmental Effects,” Wired, 19 October 2021, https://www.wired.com/story/the-faa-weighs-the-spacex-launch-sites-environmental-effects/
28 Smith, P.; J. Davidson; “How the Optus Breach Will Change Corporate Australia Forever,” Financial Review, 30 September 2022, https://www.afr.com/technology/how-the-optus-breach-will-change-corporate-australia-forever-20220929-p5bm1p
29 Ibid.
30 Ibid.
31 Taylor, C.; “GDPR at Risk of Failing Due to Underfunding of Regulators, Study Finds,” The Irish Times, 27 April 2020, https://www.irishtimes.com/business/technology/gdpr-at-risk-of-failing-due-to-underfunding-of-regulators-study-finds-1.4238927
32 Salvino, M.; “Analysis: How Will the FTC Get Its Privacy Mojo Back in 2022?”, Bloomberg Law, 1 November 2021, https://news.bloomberglaw.com/bloomberg-law-analysis/analysis-how-will-the-ftc-get-its-privacy-mojo-back-in-2022
33 Ibid.
34 Healy, T.; “The Unanticipated Consequences of Technology,” Markkula Center for Applied Ethics, Santa Clara University, Santa Clara, California, USA, https://www.scu.edu/ethics/focus-areas/technology-ethics/resources/the-unanticipated-consequences-of-technology/
35 Watson, M.; “Systems Engineering Principles,” Guide to the Systems Engineering Body of Knowledge, https://www.sebokwiki.org/wiki/Systems_Engineering_Principles
36 Ibid.
37 The Pennsylvania State University, State College, Pennsylvania, USA, “Emerging, Converging, Disruptive Technologies,” https://www.e-education.psu.edu/eme807/node/8
GUY PEARCE | CGEIT, CDPSE
Has an academic background in computer science and commerce and has served in strategic leadership, IT governance and enterprise governance capacities. He has been active in digital transformation since 1999, focusing on the people and process integration of emerging technology into organizations to ensure effective adoption. Pearce maintains a deep interest in data and their disciplines that accelerated with the launch of his high school data start-up many years ago. He was awarded the 2019 ISACA® Michael Cangemi Best Book/Author award for contributions to IT governance, and he consults in digital transformation, data and IT.