How should we think about Complexity? Is it complicated?
Article written by Mark Easdown
Business Planning, Mental Models, Ways of Thinking & Working
“A complicated system is the sum of its parts. You can solve problems by breaking things down and solving them separately. In a complex system, the properties of the whole are the result of interaction between the parts and the linkages and the constraints. In fact, in a complex system how things connect is more important than what they are. So, the properties of that emergent pattern can never be decomposed to the original parts.” David Snowden
“The problem is complexity (in financial markets) …we cannot prepare for every thread of causality through every interaction; in the speed of the event we find there is no time to make adjustments.” Richard Bookstaber
“Nature likes to over-insure itself. Layers of redundancy are the central risk management property of natural systems.” Nassim Taleb
In the mid 1980s, a school of thought emerged around “Complexity” and “Complex Adaptive Systems” with the formation of the Sante Fe Institute, formed in part by former members of Los Alamos National Laboratory. The institute drew from multi-disciplinary domains and insights of : economics, neural networks, physics, artificial intelligence, chaos theory, cybernetics, biology, ecology and archaeology. Theories on Complexity and Complex Adaptive systems sought to develop common frameworks and understandings of physical and social systems that was an alternate to more linear and reductionist modes of thinking. Members sought to better understand spontaneous, self-organising dynamics and found examples in the;
Natural World - Brains, Immune Systems, Ecologies, Cells, Developing Embryos and Ant colonies
Human World – Political parties, Scientific communities and in the economy
Why bother?
Just say you and your team are facing a complicated problem, then you may break down the problem into component parts, build an expert team internally or partner with external consultants, you may take a data driven or fact-based approach, you may identify best practices create a strategic plan and solve the problem incrementally. However, is that all? Is there a one size fits all solution to problem solving?
What if your problem could be categorised with traits such as;
Levels of uncertainty, ambiguity, unpredictability, dynamic interfaces - many diverse and independent parts that were interrelated, interdependent and linked through many interconnections into a network
The properties of the whole cannot be predicted from the behaviours of the component parts, in fact the network of many components may be gathering information learning and acting in parallel in an environment produced by these interactions – the system co-evolves within its environment
There may be significant political, social or external influences
“Wise executives tailor their approach to fit the complexity of the circumstances they face.” David Snowden & Mary Boone
Where in the real world might it be useful to have sound thinking about complexity and solving problems?
In Project Management
Programs of work and problems to solve come in a variety of forms, at the simpler end of spectrum process re-engineering and best practices serve us well to achieve desired outcomes. Yet increasingly, our problems to solve are complicated, we need to analyse things to figure out what to do and cause and effect are distanced. Complex projects are harder still, they display behaviours such as self-organising, emergent properties, non-linear and phase transition behaviours. We need a different mindset, structure and strategy to wrestle with these problems.
In Financial Markets
These are complex adaptive systems, tightly coupled with unexpected feedback loops, with investors of different investment styles & horizons, the sum of the parts will not explain the whole in a linear manner, there are infrequent extreme price moves, not normally distributed.
In Nature
Complex collective behaviours are displayed when individual ants forage for food and lay down a pheromone trail on the way out from the colony and if successful finding food lay down even more on way back to the colony. Other ants follow this stronger pheromone trail to the food adding their pheromone. So, the pheromone trail becomes the whole colonies best path to food, it is an ant colony optimisation algorithm. Interestingly, this insight has helped form the basis of swarm intelligence and a wide array of solutions across routing and scheduling problems and bayesian networks.
The twenty-first century will be the "century of complexity" Stephen Hawking
COMPLEX PROGRAMS
“Strategy in complex systems must resemble strategy in board games. You develop a small and useful tree of options that is continuously revised based on the arrangement of the pieces and the actions of the opponent. It is critical to keep the number of options open. It is important to develop a theory of what kinds of options you want to have open” - John H Holland
In complex situations "cause and effect are only coherent in retrospect and do not repeat" - Sarah Sheard
Complex problems to solve are unique and they challenge some of the traditional approaches to program and risk management thinking, which may emphasise a need to identify risks in order to control them or completely plan and control programs of work. Examples of complex programs may include: computer systems and networks, buildings, bridges, planes, ships and automobiles.
Let’s take a look at what makes complex programs unique;
Sophisticated structures with many component parts interacting with each other, giving a degree of uncertainty whereby you may not know what you don’t know until it occurs
Unknowable interdependencies across domains, a need for agility and structures that favour the decentralised and local to the centralised approach.
There may be interfaces with complementary projects which present challenges in scheduling of these interconnected systems, teams and resources
The environment may have a political realm where new government decisions or public policy arises
So, what is a desirable mindset for complex programs?
A Forward focus, a willingness to proactively manage project development and critical issues through agility, collaboration and adaptability. You may need nuanced responses and local innovation.
Analysis of likely origins of complexity and thinking through dependencies, seek critical junctions, vulnerabilities & countermeasures. Contingency planning around time, buffering on sequencing, budget and people skills
Dynamic reporting and monitoring, a willingness to pick up early warning signs and take corrective actions
Communications will be dynamic, real time & high visibility (as small changes can have oversized consequences amplified by scale of some projects)
Program planning may have both a single view and multiple integrated project schedules
Cynefin is a framework to deal with predictable and unpredictable worlds (David Snowden)
In 1999, David Snowden described a framework and problem-solving tool which helps to adjust management style to fit circumstances, and has relevance across product development, marketing, organisational design and BCP/DR and crisis management. The framework had 5 domains;
OBVIOUS. Options are clear, steps to success are known, variables well known, cause-effect relationships are apparent, you are able to assess the situation, follow a procedure, categorise its type and base your response on best practice (processes & procedures) and feasible to achieve best possible result. Examples: Product mass production, cooking with a recipe, known scientific issues, known legal issues.
COMPLICATED. Solutions not obvious to everyone but most variables involved are well known, cause-effect relationships are apparent, you may assess a situation, build a diverse team or utilise experts to deliver the best response. The best that can be achieved is a good result, maybe not the best result. Examples: Existing product enhancements, coaching a team, adopting new approaches, hiring process.
COMPLEX. The context is often unpredictable, many factors uncertain, many variables may intervene, data may be incomplete, it may not be possible to determine right options, make predictions or find cause-effect relationships, there may be multiple methods to address issues. Exploring what has a proven record in past situations, small tests or business experiments, simple guidelines, brainstorming, innovation and creativity may drive solutions. Examples: weather predictions, stock markets, poker, epidemic controls.
CHAOTIC. The situation is where nobody knows what to expect, anything can happen, it is impossible to make predictions. You may have to act towards the urgent and important, then check and evaluate result before responding to that result and acting again. Examples: Innovate new products, anything which predicts people’s preferences or behaviours, crisis event and crisis management, warfare
DISORDER. The situation is not known, you need to firstly move to a known domain & gather more information.
COMPLEXITY IN FINANCIAL MARKETS
“In the last few years the concept of self-organising systems – of complex systems in which randomness and chaos seem spontaneously to evolve into unexpected order – has become an increasingly influential idea that links together researchers in many fields, from artificial intelligence to chemistry, from evolution to geology. For whatever reason, however, this movement has so far largely passed economic theory by. It is time to see how the new ideas can usefully be applied to that immensely complex, but indisputably self-organising system we call the economy” - Paul Krugman 1996
“By one estimate, 90% of international transactions were accounted for by trade before 1970, and only 10% by capital flows. Today, despite a vast increase in global trade, that ratio has been reversed, with 90% of transactions accounted for by financial flows not directly related to trade in goods and services.” - Didier Sornette 2003
“Fundamental analysis seeks to establish how underlying values are reflected in stock prices, whereas the theory of reflexivity shows how stock prices can influence underlying values. One provides a static picture, the other a dynamic one.” - George Soros
Financial markets have all the basic components of complex adaptive systems, namely:
Investors have differing investment strategies and horizons from trading at the speed of light to long term cyclical horizons. They take external information and combine it with their own strategic intent and these compete in financial markets => this is adaptive decision making
Financial markets is the aggregation of large-scale collective decision making and actions => these are developing, complex and emergent
Financial markets exist in a non-equilibrium state , are non linear, they experience non-frequent extreme price moves with the aggregate behaviour more complex than would be predicted by the sum of the individual parts.
Financial Markets are subject to feedback loops, where the result of one iteration becomes an input of next iteration
So, how has the emerging knowledge of complexity and financial markets framed regulators thinking?
The global financial crisis highlighted the complexity, leverage, inter-connected and tightly coupled of financial markets. In response we have seen;
Efforts to reduce interconnectedness (intra-day local trading halts, regional collateral exchanges)
Enhanced capital rules (increased contingency buffers and incentives for some activities to be managed by non-bank sector & efforts to reduce concentration of risks)
Enhanced liquidity rules (increase quantum and quality of contingency buffers)
Speed, agility and quantum of central bank and treasury initiatives to address market panics and crisis
A re-think of the rule-making complexity and mental models applied to finance;
“Modern finance is complex, perhaps too complex. Regulation of modern finance is complex, almost certainly too complex. That configuration spells trouble. As you do not fight fire with fire, you do not fight complexity with complexity. Because complexity generates uncertainty, not risk, it requires a regulatory response grounded in simplicity, not complexity. Delivering that would require an about-turn from the regulatory community from the path followed for the better part of the past 50 years. If a once-in-a-lifetime crisis is not able to deliver that change, it is not clear what will.” - Andrew Haldane Bank of England Speech 2012 “ The Dog and the Frisbee” [https://www.bis.org/review/r120905a.pdf ]
COMPLEXITY IN NATURE
In her TED Talk, Deborah Gordon: The emergent genius of ant colonies: highlights an example of a complex adapative system with no central control or management in an ant colony: [ https://www.youtube.com/watch?v=ukS4UjCauUs]
So, what is the strategy of the ant colony to constantly adapt to its complex environment? As per Deborah Gordon studies;
The ant colony allocates simple roles
At any given time 25% are patrolling, foraging and doing maintenance, 25% are inside with queen ant doing maintenance and looking after larvae, and finally 50% appear to be contingency and in reserve, able to surge as required to collect more food, patrol or more maintenance.
Communications are not centralised, they are dynamic, simple and local rules to adapt to emergent environment
The process is noisy, messy, imperfect and requires individual dynamic communications
Ant colonies can learn at the individual level by trial and error over many generations but this can nurture collective memory and problem-solving skills. The local instructing the central.
“So, the key to unlocking the efficiency of a leaderless system will rely on, among other things: clear role definition, flexible task allocation, a sense of responsibility toward the group, and shared understanding and response to communication patterns. Organizations would need to make an incredible investment in their employees, and vice versa.” Amanda Silver – Organising complexity – How Ant colonies self-manage. [https://medium.com/swlh/organizing-complexity-how-ant-colonies-self-manage-50455358f3cd]
How we should think about complex domains is still evolving, a multi-disciplinary lens across research and practice has been adding to this knowledge pool for decades. It is a vital enquiry for humankind, especially as our challenges become more complex to solve and our climate is as a complex adaptive system.
‘“he climate is a common good, belonging to all and meant for all. At the global level, it is a complex system linked to many of the essential conditions for human life.”- Pope Francis 2015
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Article written by Mark Easdown
