Complexity Basics — Part IV: A Critique of the BANI Framework
(Social) complexity all the way down
Table Of Contents
1. The BANI framework
↳ 1.a. VUCA has become so commonplace that the information value tends towards zero
↳ 1.b. The world is now so chaotic that we need a new language: BANI
↳ 1.c. What does the acronym BANI mean exactly?
↳ 1.d. Cascio’s conclusion
2. The pros and cons of BANI
↳ 2.a. The pros
↳ 2.b. The cons
3. Conclusion
↳ 3.a. BANI has failed
↳ 3.b. What is needed is a deeper understanding of complexity in general and social, especially organizational complexity in particular
This is the fourth and final part of our mini-series on the basics of (social) complexity.
The starting point of this series were two questions that came up on- and offline when talking about VUCA (Volatility, Uncertainty, Complexity and Ambiguity) challenges for present-day organizations in the context of Business Process Management (BPM).
- Question 1: Is VUCA outdated so that we need new acronyms / models such as Jamais Cascio’s BANI (Brittle, Anxious, Non-Linear and Incomprehensible) framework?
- Question 2: Is the reference to complexity in general and VUCA in particular rather trivial because we‘ve always lived in such environments?
We answered the second question in part III of this series —
VUCA is both trivial and somehow misleading because:
- On the one hand, our species has always lived in complex ecological, cellular and neural environments that have tended to overwhelm us. That’s the trival aspect.
- On the other hand, observers can describe an increase in media, technological and social complexity that has gained momentum as never before, especially since the so-called long 19th century — so far without stopping.
And this is anything but trivial!
The misleading aspect is then that VUCA considers complexity as one feature among others such as volatility, uncertainty, etc.
However, the main category is (social) complexity while volatility, uncertainty, etc. belong to just an open-ended list of features of complexity (see my Medium post (Social) Complexity Basics: Features of Complexity and the Scalability Problem for more details, especially on the non-essentialist aspect of those features).
In this post, we focus on question 1, i.e., whether it is sufficient to introduce new acronyms, or more precisely, new frameworks for sense-making such as BANI.
1. The BANI framework
The creator of the acronym BANI is the American author and futurist, Jamais Cascio, who presented this sense-making framework in the Medium post Facing the Age of Chaos in April 2020.
His argumentation is as follows:
1.a. VUCA has become so commonplace that the information value tends towards zero
The concept of VUCA is clear, evocative, and increasingly obsolete. We have become so thoroughly surrounded by a world of VUCA that it seems less a way to distinguish important differences than simply a depiction of our default condition. Using “VUCA” to describe reality provides diminishing insight; declaring a situation or a system to be volatile or ambiguous tells us nothing new. [Cascio, F. (2020), op.cit.]
1.b. The world is now so chaotic that we need a new language: BANI
If we set VUCA aside as insufficient, we still need a framework that makes sense of not just the present world but its ongoing consequences as well. Such a framing would allow us to illustrate the scale of the disruptions, the chaos, underway, and enable consideration of what kinds of responses would be useful. Ideally, it would serve as a platform to explore new forms of adaptive strategies. Scenarios, models, and transparency are useful handles on a VUCA world; what might be the tools that would let us understand chaos? [Cascio, F. (2020), op.cit.]
1.c. What does the acronym BANI mean exactly?
- B = Brittle
- According to Cascio, a brittle system seems to be strong, but it’s not.
It’s rather non-resilient so that it collapses easily when it hits a breaking point.
- There are two reasons for a system to be brittle: there is a single point of failure and/or there is no buffer, in short: there is a lack of slack due to excess efficiency, e.g. in organizations.
- Cascio’s examples are monocultures, the resource curse, etc. - A = Anxious or rather anxiety-inducing
This refers to the feeling of loss of control, where every decision we make can have catastrophic consequences.
In this context, reference is also made to the sensationalist mass media, which are anxiety-inducing. - N = Nonlinearity
- Nonlinearity means that the effect of a cause isn’t proportional to its magnitude, or that multiple causes interact to produce an effect. This is a characteristic of both complex (adaptive) systems and systems based on deterministic chaos (see sections 2.e. and 2.f in part II of this series).
- Examples for nonlinear behavior are in particular biological systems:
Most importantly, nonlinearity is ubiquitous in biological systems. The growth and collapse of populations, the effectiveness of vaccination, swarm behavior, and, as noted, the spread of pandemics — all of these have a strongly nonlinear aspect. [Cascio, F. (2020), op.cit.]
- I = Incomprehensible
We witness events and decisions that seem illogical or senseless, whether because the origins are too long ago, or too unspeakable, or just too absurd. “Why did they do that?” “How did that happen?” We try to find answers but the answers don’t make sense. [Cascio, F. (2020), op.cit.]
Moreover, additional information is no guarantee of improved understanding. More data — even big data — can be counter-productive, overwhelming our ability to understand the world, making it hard to distinguish noise from signal. Incomprehensibility is, in effect, the end state of “information overload.” [Cascio, F. (2020), op.cit.]
1.d. Cascio’s conclusion
Given the massive and potentially overwhelming changes in all our systems (global trade and information networks, personal relationships, etc.), which tend to be (more and more) chaotic, a new language, or here: a new framework for sense-making à la BANI, is needed to describe and understand this paradigm shift.
2. The pros and cons of BANI
2.a. The pros
Jamais Cascio’s BANI post expresses a kind of time-diagnostic malaise that many of us will share. For it is triggered, for example, by:
- The proliferation of technological networks, which has massively increased the degree of social interconnectedness and social dynamics.
- The potentially dire consequences of global warming. Keywords:
- Cascading tipping points.
- An irreversible Hothouse Earth pathway. - An unprecedented information explosion as digital media became our leading media.
- The breathtaking speed of development of Artificial Intelligence (generative AIs / large language models, etc.) / robots (e.g., Unitree’s Go2 robodogs):
In addition, Cascio’s framework draws our attention to the fact that societal collapses can’t be ruled out in the near future, especially if a perfect storm occurs:
- Cascading tipping points in the Earth’s climate.
- Violent resource conflicts.
- Major wars involving the use of nuclear weapons.
etc.
Last, but not least, BANI emphasizes the importance of resilience (beyond organizational slack) so that systems have a higher probability of surviving existential crises.
2.b. The cons
- The world isn’t chaotic, it just is (overwhelming):
- If we consider the world as an irreducible ultimate horizon (in a phenomenological sense), then language-based observers have no access to it.
And if they had, its hypercomplexity would be so overwhelming that it would immediately drive them crazy (see part II in this series).
- However, even if world refers only to our planet, it makes no sense to claim that everything is chaotic — because under such highly unstable conditions, life as we know it couldn’t arise or persist.
- Moreover, our conditio moderna is that observers can observe their environments in very different (also: conflicting) ways, so that there is also no objective observation that applies equally to all observers! - All cats may be gray at night, but we shouldn’t put all systems into one dark conceptual bag,
- … so that it does not really matter whether we talk about
- simple,
- complicated,
- complex-non-adaptive,
- complex-adaptive,
- chaotic or
- really random systems (see again part II in this series),
because such a conceptual indifference leads to an epistemic mess. - This also means that it makes no sense to claim that chaos is the new, especially social/societal normal.
To be more specific, it’s one thing to say that there are, for example, mass panics from time to time (which can be studied as chaotic systems). But it would be quite another thing if there were mass panics everywhere, 24/7, 365 days a year.
On the contrary, many families, organizations and societies are still quite stable and chaotic episodes are the exception rather than the rule here! - This means further that complex (adaptive) systems (CAS) like societies, cities, organizations etc. can have chaotic transition phases from time to time without being deterministic-chaotic systems! The reason is that because of the complexity of such CAS, they are not sensitive to initial conditions, as it is the case with deterministic chaos.
- Complex adaptive systems are usually not brittle (in a BANI sense, see above). Why? The reason is simple: the higher the complexity of a CAS, the more options it has to react to changes in its turbulent environment.
In brief:
- Simple / (more or less) complicated systems tend to be brittle.
- CAS are much more resilient due to their higher degree of internal complexity, their adaptation and learning mechanisms and their self-organization.
That said, nevertheless, CAS can be destroyed!
However, what is observed by some external observers as a systemic collapse may also just be a systemic transformation after a more or less chaotic transition phase (see, for example, the so-called Fall of the Western Roman Empire and Walter Scheidel’s excellent study Escape from Rome (2021)). - Apart from that, nonlinearity and ultimate incomprehensibility (due to the failure of linear-reductionist causal thinking and conventional statistics) are typical features of complex (adaptive) systems.
In brief, his has absolutely nothing to do with a paradigm shift towards chaos as the new normal! - And, finally, what about anxiousness(-inducing)?
Well, some language-based observers can become anxious — others won’t— when observing their environments.
Usually, it’s best to keep a cool head and focus on what you can control, and ignore what you can’t.
Moreover, it’s always a good idea to avoid mass media alarmism because this is a well-known method used by the mass media to try to get / hold our attention: no more, no less.
3. Conclusion
3.a. BANI has failed
BANI as a sense-making framework has failed, because chaos isn’t a new normality, at least not on the social or societal levels. Here, everything is the same as before: a media, technological and societal increase in complexity, which has been constantly accelerating since early modern times - and temporary chaotic transitional phases in complex adaptive systems don’t change that!
Note:
But maybe we need to talk about chaos again when it comes to
- An AI-associated singularity.
- A climate catastrophe associated with cascading tipping points.
- Or some kind of perfect storm as mentioned above.
That said, VUCA as an acronym isn’t very helpful either because it’s trivial and misleading.
3.b. What is needed is a deeper understanding of complexity in general and social, especially organizational complexity in particular
- It probably starts with the awareness that simplicity is only beautiful when applied to complicated problems / domains / systems, but that an uncritical cult of simplicity with its:
- Linear, reductionist-analytical logic.
- Bold, but naive interventions.
- Illusions of control.
- Search for single principles / ideas from which everything else can be logically derived.
- Cult of unambiguity.
- Causal thinking.
- etc.
can do more harm than good when applied to complex problems / domains / systems. - And that, in turn, requires understanding the difference between the complicated and the complex, and not simply constructing an indistinguishable hodgepodge.
- In other words, it’s a good start to:
- Be familiar with the Cynefin framework (see part I of this series).
- Distinguish between various system references (see part II of this series).
- Dive deeper into various approaches about:
-> Complexity and business (for example, Rick Nason 2017, It’s Not Complicated: The Art and Science of Complexity in Business).
-> Organizations as complex systems (e.g., Stefan Kühl 2021, Organizations: A Systems Approach, Niklas Luhmann’s classic Organization and Decision, etc.).
-> Complexity and management.
-> Complexity (and) leadership.
- Develop a non-interventionist view of organizational cultures and organizational change, which is about setting contextual factors (resource allocation, etc.) and letting the actual complex culture/organizational dynamics unfold on their own (the analogy is a gardener who doesn’t force plants to grow, but simply enables them to grow).
- Combine organizational complexity with business agility:
- Emphasize and elaborate non-knowledge regarding complex phenomena, which are ultimately characterized by their very incomprehensibility.
- Understand that the answer to increasing complexity in a turbulent organizational environment is often to first increase the internal complexity of an organization before you can increase agile speed — otherwise it’s just blindly rushing forward without knowing what you’re doing.
Note:
See Max Boiset / Bill McKelvey’s Law of requisite complexity (from 2011), which follows William Ross Ashby’s cybernetic Law of requisite variety.
The former states that the complexity of a system must match the complexity of its environment.
- etc.
Thanks for reading and, hopefully, see you in the next post!
Author for WAITS Software und Prozessberatungsgesellschaft mbH, Cologne, Germany: Peter Bormann — November 2023.
