COMPLEXITY & IB

Managing Under Complexity

Introduction

  • Overview of Complexity in Management:

    • Simplification in a complex situation can often lead to dangerous and costly outcomes.

    • Proposes that executives require a set of effective practices and humility to handle complexity.

    • Quote from Albert Einstein: "Make everything as simple as possible, but no simpler."

The Nature of Complexity

  • Definition of Complexity:

    • Described as "the quality of being intricate and compounded."

  • Complex Systems:

    • Operate in unexpected ways and often cannot be predicted in advance.

  • Cognitive Limitations:

    • According to psychologist George A. Miller, human short-term memory can only process around 7 elements at once (the "magical number seven, plus or minus two").

    • With additional complexities, previous elements may be forgotten.

  • Impact of Interruptions:

    • Research shows that interruptions can disrupt cognitive processing for up to 25 minutes.

Challenges for Executives

  • Sensemaking Paradox:

    • Understanding requires filtering complex information, which can lead to missing key elements.

    • Example: In a video counting basketball passes, observers often miss a gorilla suited dancer due to focus.

Unintended Consequences

  • Understanding how focused attention can result in missing critical phenomena in complex situations:

    • Example: UK Parliament Living Standards: Reimbursement program led to inappropriate claims (e.g., dog food, TV sets).

    • Positive unintended consequences can also arise, like e-learning technologies succeeding during school shutdowns due to swine flu.

  • Aggregation of Individual Rational Decisions:

    • Example: Financial meltdown of 2008 due to individual incentives that collectively produced disastrous outcomes.

  • Historical example of over 130 firms entering a market for Winchester hard drives, leading to competitive saturation.

Rare Events

  • Complex systems struggle with rare events (e.g., natural disasters) that disrupt established order.

    • Volcanic Eruptions and Air Traffic: The 2010 Eyjafjallajökull eruption caused unprecedented flight disruptions due to lack of previous planning.

Limits of Quantification

  • The Central Limit Theorem: Statistics can mislead when not all phenomena conform to expected distributions.

    • Normal Distribution Expectations: Many business models rely on averages, neglecting the importance of outliers.

    • Examples include historical financial failures due to reliance on flawed statistical models.

Irreversibilities and Network Effects

  • Some early decisions in systems create irreversible impacts, like the adoption of the QWERTY keyboard due to network externalities.

    • Paul A. David's findings on how technical interrelatedness, economies of scale, and quasi-irreversibility preserved QWERTY's dominance.

Effective Strategies to Address Complexity

  • Avoiding Oversimplification:

    • Simpler rules may not be effective in complex systems, often requiring counterintuitive strategies.

  • Buffers of Time and Space:

    • Allowing for time delays can help systems cope better with complex decisions, as demonstrated in emergency medical triage.

  • Reducing Vulnerability through Interdependence Management:

    • Organizations can reduce risk through redundancy and modular systems design allowing flexible responses.

Addressing Oversimplification in Sensemaking

  • Strategies to combat the oversimplification trap:

    • Explicitly label assumptions and encourage challenge.

    • Use techniques like Discovery Driven Planning, which require frequent testing of assumptions at key checkpoints.

  • Case Study: SAP’s Business ByDesign Failure:

    • Initial assumptions about engineering and comprehensive services led to failure, demonstrating the need for re-evaluation.

Importance of Communication and Diversity

  • Increased communication and diverse perspectives enhance organizational decision-making in complex situations.

  • Example: Political scientist Aaron Wildavsky emphasizes designing systems for resilience rather than simply prevention.

Anticipation vs. Resilience

  • Two approaches to managing risks:

    • Prevention: Targets mitigating negative outcomes.

    • Resilience: Emphasizes launching adaptive processes to respond when potential failures occur.

  • Case Study: Victor Talking Machine Company's Radio:

    • A classic case of prevention leading to failure when faced with disruptive technology.

Shared Values for Guidance

  • In unpredictable situations, micromanagement often leads to confusion.

  • Nordstrom's Approach: Emphasizes shared values over rules, allowing employees to use judgment (notably their famous customer service example).

Tools to Combat Quantitative Model Risks

  • Counterfactuals: Explore alternative scenarios to enhance understanding of complex interactions.

    • Examples include assessing Apple's iPod and iTunes link in market leadership.

  • Triangulation: Gathering diverse data sources to validate conclusions.

Real Options Approach

  • Investing in real options minimizes risk while creating avenues for learning and growth.

  • Concept emphasizes small initial investments allowing organizations to adapt and respond to uncertainty.

  • Guidelines for Real Options:

    • Emphasize high upside potential, small investment prerequisites, and flexibility to halt investments.

Advantages of Small Wins

  • Initiating small-scale projects yields immediate feedback and facilitates adaptive revisions.

  • Small Wins Concept: Minimizes causal misattribution and requires fewer resources, thus increasing organizational resilience.

Conclusion

  • Instead of providing simple solutions, the navigation of complex situations necessitates employing discontinuous practices and adhering closely to a paradigm of humility while managing complexity.

🧩 What Is Complexity — Key Concepts

Three properties that determine complexity:

  1. Multiplicity – number of interacting elements (e.g. global suppliers, consumers, governments).

  2. Interdependence – how connected those elements are (e.g. trade and financial linkages).

  3. Diversity – degree of variation among elements (e.g. cultural, institutional, or economic diversity).

→ The higher these are, the harder it becomes to predict or control outcomes.

💡 Example Sections Explained & Related to IB

1. The Sensemaking Paradox

Managers simplify reality to understand it—but this filtering can make them miss crucial signals.
Example: Sony focused too narrowly on disk-based tech and missed the streaming revolution.

IB Example:
A multinational like Volkswagen focusing only on European consumer expectations failed to predict rapid EV adoption in China and the U.S., losing early EV market share. Over-focusing on “what we already know” in global markets can blind firms to new local trends.

2. Unintended Consequences

Actions in complex systems create ripple effects—some beneficial, many harmful.
Example: U.K. parliament’s expense reimbursements → public scandal.

IB Example:
When fast fashion firms (e.g. H&M, Zara) outsourced production to Bangladesh to lower costs, it unintentionally led to reputational crises after factory disasters (e.g. Rana Plaza 2013). Global interdependence means well-intended efficiency can trigger ethical backlash.

3. Rare Events

Unusual disruptions reveal hidden vulnerabilities.
Example: The Icelandic volcano grounded air travel globally.

IB Example:
The COVID-19 pandemic was a rare event that disrupted global supply chains and forced firms to rethink dependence on single regions (e.g. China). It accelerated supply chain diversification (“China+1”) and digital trade adoption.

4. Quantification

Managers rely on models assuming “normal” patterns—but many global systems don’t behave normally.
Example: Financial models failed to predict 2008 crisis.

IB Example:
Global investment models based on GDP averages missed the rapid rise of informal economies and digital trade in developing countries, leading to poor market entry strategies.

5. Irreversibilities & Network Effects

Early choices “lock in” systems (e.g. QWERTY keyboard).
Example: Once standards or technologies spread, switching becomes hard.

IB Example:
Once Visa and Mastercard established global payment networks, new fintechs struggled to break in — path dependency made the system nearly irreversible. Same applies to Apple’s App Store ecosystem dominating mobile app markets worldwide.

🧭 Facing Down / Managing Complexity (Second Half)

Buffers of Time and Space

Create flexibility through delays or redundancy.
Example: Toyota’s inventory buffers help absorb supply shocks.

Addressing Oversimplification

Make assumptions explicit and test them (Discovery-Driven Planning).
Example: Unilever regularly re-tests sustainability assumptions in different regional markets.

Prevention vs. Resilience

Don’t only prevent crises—build resilience to adapt after them.
Example: Apple built multiple suppliers across Asia to adapt quickly to disruptions.

Importance of Shared Values

Rules can’t cover every situation—values guide decentralized action.
Example: Patagonia empowers employees globally using shared environmental values instead of rigid policies.

Counterfactuals & Triangulation

Test “what if” scenarios and use multiple data sources.
Example: IMF and World Bank model economic shocks using diverse regional data to avoid over-reliance on one assumption.

Real Options

Invest small amounts in multiple future opportunities—learning through small wins.
Example: Google X (Moonshot projects) invests in many small experiments to manage uncertainty and learn fast.