Foundations

What is leadership and how should leaders think?

Andy Weeger

Neu-Ulm University of Applied Sciences

Learning objectives

After completing this unit, you will be able to:

1. Distinguish leadership from management and explain why the digital age demands distinct leadership capabilities.
2. Explain what mental models are and why a latticework of models improves decision-making under complexity.
3. Apply core generic, systems thinking, and human behavior mental models to leadership scenarios.
4. Evaluate the limitations of any single mental model using the “map vs. territory” principle.

Introduction

Digital leaders empower people with vision, understanding, clarity and agility. Waltraud Glaeser, Leadership Coach

Everyday leadership — We have made leadership about changing the world, and there is no world in the world that does not begin with changing yourself. Drew Dudley

This course explores what it takes to lead in the digital age. Rather than treating leadership as a single skill or trait, we will build a diverse toolkit of mental models —a latticework— that helps you navigate the complexity, ambiguity, and rapid change that define modern organizations.

Definitions

Progression

From leadership to the digital age to digital leadership.

Discussion

What is leadership?

Leadership defined

A leader … is like a shepherd. He stays behind the flock, letting the most nimble go out ahead, whereupon the others follow, not realizing that all along they are being directed from behind. Nelson Mandela

Leadership

Winston & Patterson (2006, p. 8) define leadership as:

One or more people who selects, equips, trains, and influences one or more follower(s) who have diverse gifts, abilities, and skills and focuses the follower(s) to the organization’s mission and objectives causing the follower(s) to willingly and enthusiastically expend spiritual, emotional, and physical energy in a concerted coordinated effort to achieve the organizational mission and objectives.

This integrative definition by Winston & Patterson (2006) captures several key aspects:

• leadership is a relational process (it requires followers),
• it is purposeful (directed toward organizational objectives),
• and it is voluntary (followers expend energy willingly).
• We will return to this definition repeatedly throughout the course as we unpack what each component demands of leaders.

Management

Kotter (2017) differentiates leadership from management as follows:

Management is about coping with complexity,
while leadership is about coping with change

Management brings order and predictability to a situation. But that’s no longer enough—to succeed, companies must be able to adapt to change. Leadership then, is about learning how to cope with rapid change (Kotter, 2017).

• Management involves planning and budgeting.
  Leadership involves setting direction.
• Management involves organizing and staffing.
  Leadership involves aligning people.
• Management provides control and solves problems.
  Leadership involves providing motivation.

Discussion

Why speak about
digital leadership?

What is specific about the digital age?

Context of leadership

The digital age is coined by digital technologies (e.g., Social media, Mobile computing, Analytics/big data, Cloud computing — SMAC) that are inherently disruptive and that cause major changes (Karimi & Walter, 2015).

Verhoef et al. (2021) identifies three phases of these disruptive changes:
digitization, digitalization, and digital transformation.

• Digitization is the “the process of converting analog signals into a digital form, and ultimately into binary digits (bits)” (Tilson et al., 2010, p. 749).
• Digitalization is “a sociotechnical process of applying digitizing techniques to broader social and institutional contexts” (Tilson et al., 2010, p. 749).
• Digital transformation is “a process that aims to improve an entity by triggering significant changes to its properties through combinations of information, computing, communication, and connectivity technologies” (Vial, 2019, p. 118).

Discussion

What are specifics of the digital age that require distinct leadership skills?

In a small group, take 10 minutes and find 2 characteristics of the digital age and a related leadership skill.

Disruptive potential

Digital technologies do not develop linearly, but exponentially, which is leading to significant changes in people’s behavior, attitudes, and expectations.

Digital technologies do not develop linearly, but exponentially (Schreckling & Steiger, 2017):

• Hardware becomes ever more powerful, small, and thus ubiquitous (Moore’s Law¹).
• As every consumer and every machine (‘Things’) is able to be connected, established (rules of) individual and organizational behavior are disrupted (i.e., hyper connectivity)
• Network effects lead to a superlinear increase in value by connecting systems, processes, and users (Metcalfe’s law²).
• Comparatively low barriers to entry

This fuels and is fueled by changes in people’s behavior, attitudes, and expectations, e.g.,

• The speed with which people adopt new technologies
• The urge to use connected devices to take action on whatever we need or want right now

We’re not going online, we’re living online. Jerry Dischler, VP of AdWords Product Management at Google

Acceleration of VUCA

Volatility
Uncertainty
Complexity
& Ambiguity

The term VUCA was introduced by the US Army War College to describe a volatile, uncertain, complex and ambiguous environment. It was first used by the Pentagon to describe the terrorist attacks of 11 September 2001. A few years later it was used by strategic business leaders to describe the chaotic, turbulent and rapidly changing business environment that has become the ‘new reality’ (“VUCA,” n.d.).

Characteristic of a VUCA environment is not so much the disruptive upheavals caused by (digital) technology, but rather the speed and scale of disruption specific to the digital age.

• Volatility can be defined as the nature, speed, volume and magnitude of change that does not follow a predictable pattern (Sullivan, 2012). This is turbulence; a phenomenon that is occurring more frequently and more rapidly than in the past. Typically, market demand in many businesses was already increasingly volatile and unpredictable before the Corona virus crisis, and will be even more so after the containment.
• Uncertainty can be seen as the lack of predictability of issues and events (Sullivan, 2012). Past issues and events cannot be used to predict future outcomes due to volatility. As a result, forecasting becomes extremely difficult and decision making very challenging for most executives.
• Complexity is the difficulty in understanding the causes and mitigating factors (both inside and outside the organisation) involved in a problem (Sullivan, 2012). The layer of complexity, added to the turbulence of change and the absence of past predictors, adds to the difficulty of decision making and leads to confusion.
• Ambiguity is the result of the confusion mentioned above. The lack of clarity about the meaning of the change slows down the decision making process to the point of freezing the status quo (Sullivan, 2012).

Digital leadership

Digital technology has changed and is changing organizations’ structure, work environment, processes and culture, in an irreversible way, creating new challenges that leaders have to face.

Organizational leaders must work to ensure that their organization is capable of responding to the disruptions associated with the use of digital technologies and of responding to the changes (Vial, 2019), e.g., by

• dissolving inertia and resistance to change,
• leading employees to assume roles that were traditionally outside of their functions,
• acting as boundary spanners that foster close collaboration between business and IT functions,
• depending on analytical skills to solve increasingly complex business problems,
• ensuring that digital technologies are properly leveraged (i.e., used).

Digital leaders empower people with VUCA — vision, understanding, clarity and agility.

Mental models

We have established that the digital age creates unprecedented complexity and that leaders need vision, understanding, clarity, and agility to navigate it. But how do you develop these capabilities? The answer lies in how you think —specifically, in the quality and diversity of the mental models you bring to every situation.

Opening quote

In life and business, the person with the fewest blind spots wins. Shane Parrish

The decision-making crisis

Leadership is challenged with information overload (2.5 quintillion bytes created daily), increasing complexity of systems, and a VUCA environment (volatile, uncertain, complex, ambiguous).

In today’s world we need to deal with an unprecedented scale of information. 2.5 quintillion bytes daily means our intuitive approaches to processing information are inadequate. Digital leaders need to cope with VUCA as the “new normal”, which is not a temporary state but the permanent condition of modern leadership.

According to Parrish (2020) information overload, system complexity, and VUCA environments cause significant decision-making challenges in several ways:

• When faced with overwhelming information and complexity, we tend to filter based on our existing biases, potentially missing critical variables that would lead to better decisions (i.e., multiplied blind spots).
• When information exceeds our processing capacity, we tend to rely on simplistic heuristics or familiar models even when they don’t fit the situation rather than the thoughtful application of diverse mental models.
• The parable of the blind men and the elephant becomes even more relevant as in complex systems, each specialist sees only their part of the problem, but no one sees the whole picture.
• In complex systems, the consequences of decisions may be distant in time and space, making it harder to “keep our feet on the ground” and learn from experience (i.e, feedback delay).
• VUCA environments make it particularly difficult to anticipate the “second-, third-, and higher-order consequences of various choices”, which are crucial for decision-making.

Paradox

We’re trained to become specialists, yet specialization itself creates blind spots. Parrish (2020)

Technical expertise alone is insufficient for effective digital leadership.

This is why we need mental models from multiple disciplines
— they help us see beyond our specialization.

How many IT implementations have failed not because of technical issues but because of human factors, business process misalignments, or organizational dynamics?

The problem with specialization

Figure 1: Illustration of the blind men in the classic parable (Parrish, 2020, p. 21)

Just as the blind men each touch a different part of the elephant—perceiving it as a wall, a rope, a fan, a tree, or a spear based on their limited contact—we tend to interpret complex problems solely through our specialized expertise. Whether economists, engineers, physicists, mathematicians, biologists, or chemists, we grasp partial truths about reality while missing the complete picture that would be visible if we combined our perspectives (Parrish, 2020, p. 21).

• Domain expertise creates tunnel vision
• Overuse of familiar tools (“to a hammer, everything looks like a nail”)
• Disciplinary silos limit innovation

Technical expertise alone is insufficient for effective digital leadership. This is why we need mental models from multiple disciplines - they help us see beyond our specialization.

Further reading

Tetlock & Gardner (2015) emphasize that better decision-making comes not from specialized knowledge alone but from cultivating systematic thought processes that more accurately represent reality. For instance, their expert political judgment studies show specialists make worse predictions than generalists.

This further highlights how developing better mental models isn’t just philosophical—it produces measurably superior outcomes when facing uncertainty.

Latticework thinking

Mental models

Johnson-Laird (1983) shows that humans don’t naturally think using the rules of formal logic (like syllogisms or propositional calculus). Instead, we construct simplified mental representations or “models” of situations and mentally simulate what might happen within those scenarios.

A mental model is a cognitive representation such as a conceptual framework or worldview that helps us understand and interpret the world (Jones et al., 2011).

They function as

• filter for relevant information,
• framework for interpretation (e.g., cause-and-effect dynamics), and
• guide for decision-making.

Mental models can change over time through learning.

Simple example: When you drive a car, you have a mental model of how turning the steering wheel affects direction. You don’t think about it consciously — it’s internalized.

Mental models are not just conscious frameworks — they operate at both conscious and unconscious levels, shaping what we notice and how we interpret it. The quality of our decisions depends on the quality and diversity of our mental models.

Discussion

Have you ever seen something fail because people were looking at a problem in a narrow way?

Prompt students to share examples from their own experience — failed IT projects, misguided strategies, or everyday situations where a single-perspective approach led to poor outcomes. Steer the discussion toward the insight that complex systems cannot be understood through any single lens — they require multiple viewpoints, just like the elephant in the parable.

The latticework approach

Worldly wisdom requires models from all important disciplines (Parrish, 2020).

You’ve got to have models in your head. And you’ve got to array your experience—both vicarious and direct—on this latticework of models. Charlie Munger, Partner at Berkshire Hathaway (1924-2023)

Mental model latticework
(Parrish, 2020, p. 22)

When findings from different disciplines support the same conclusion, it strengthens the validity of that conclusion. And combining insights across fields can reveal patterns and principles invisible within any single discipline (remember the tale of the elephant).

Example: Netflix uses game theory principles to structure their recommendation algorithms, these create incentive structures for continued engagement, these incentives are implemented through specific UX design patterns, these designs leverage behavioral economics principles like hyperbolic discounting (valuing immediate rewards over future ones)

Mental models x leaders

Introduction

Mental models provide the understanding of how a system works and allow us to use heuristics to quickly navigate within that system.

Mental models are the key to making heuristics fast, frugal and accurate strategies. Such simple mental shortcuts in turn, enable rather than restrict decision-making under uncertainty (Gigerenzer et al., 2022).

Generic mental models

Generic mental models are models that are broadly applicable across multiple domains, disciplines, and situations, rather than being specific to a single field or context.

According to Parrish (2020) some effective generic mental models are

first principles thinking, second-order thinking, probabilistic reasoning, inversion, and Occam’s razor.

First principles

Elon Musk attributes Tesla and SpaceX’s innovations to breaking problems down to fundamental truths rather than reasoning by analogy — this is how they reimagined electric vehicles and rocket economics.

Second-order thinking

Most people stop at first-order consequences, but digital leaders need to ask ‘And then what?’ repeatedly.

Probabilistic reasoning

In environments of uncertainty, thinking in probabilities rather than certainties helps avoid both overconfidence and paralysis.

Inversion

Instead of asking ‘How can we make this project succeed?’ also ask ‘What would guarantee this project’s failure?’ - then avoid those conditions.

Occam’s razor

When presented with competing explanations or solutions, start with the simplest one with fewest assumptions.

Systems thinking models

Systems thinking models are mental frameworks that help us understand complex systems by focusing on relationships, interactions, and emergent properties rather than isolated components.

Important system thinking mental models are

complex adaptive systems, feedback loops, emergence, and network effects.

Complex adaptive systems

Digital ecosystems behave like biological ones — they organize themselves without central control or explicit coordination and, thus, can only be influenced but not controlled through well-chosen interventions.

Feedback loops

Systems are usually coined by reinforcing loops (where A increases B which further increases A) and balancing loops (where changes trigger countervailing forces) - understanding these dynamics helps predict how systems will evolve

Emergence

The most important properties of (digital) systems often are not designed but emerge from interactions — like how simple features of social networks created entirely new social behaviors (e.g., the hashtag effect).

Network effects

Understanding Metcalfe’s Law —that the value of a network grows with the square of the number of users— explains why platforms like LinkedIn or Slack become increasingly valuable and difficult to displace.

Human behavior models

Human behavior models are conceptual frameworks that help explain, predict, and influence how people think, decide, and act. These models are particularly valuable for digital leaders who need to understand both individual psychology and group dynamics when designing systems, implementing change, or leading organizations.

Human behavior models include

incentives, cognitive bias, social and group behavior models, and learning curves.

Incentives

These models explain how rewards and punishments shape behavior such as the principal-agent problem, intrinsic vs. extrinsic motivation and hyperbolic discounting (i.e., the tendency to overvalue immediate rewards compared to future ones).

Cognitive bias

These models explain systematic errors in thinking and decision-making such as confirmation bias, framing effects, and availability heuristic (i.e., judging probability based on how easily examples come to mind).

Social and group behavior models

These models explain how people interact and organize such as status games (competition for position and recognition), reciprocity, and social proof.

Implications

Map vs. territory

The map is not the territory … the only usefulness of a map depends on similarity of structure between the empirical world and the map.³ (Korzybski, 1958).

As all models are wrong, but some are useful:

• Continually test and update models
• Maintain epistemic humility
• Seek disconfirming evidence
• Use multiple maps of the same territory

Build your latticework

Start with fundamental, versatile models, build deliberately across disciplines, and test models through application.

Parrish (2020) recommends to begin with a handful of models from different disciplines rather than many from one field. Quality and diversity trump quantity. In the beginning, focus on mental models with the broadest application - like feedback loops, incentive structures, inversion, and first principles thinking. Read widely, but with purpose. Look for concepts that explain phenomena across multiple domains.

Put an emphasis on application as the key difference between collecting mental models and building a latticework is application. Use the models learned actively in your decision-making and keep a decision journal where you explicitly note which mental models you applied and review outcomes to refine your understanding.

Approach this as a career-long project — the compound interest of mental models accumulates over decades.

Three-step approach:

1. Learn (study diverse fields)
2. Apply (use models in real decisions)
3. Reflect (record outcomes and refine)

Latticework x this course

Building your latticework — unit by unit

Throughout this course, each unit adds new mental models to your latticework. By the end, you will have built your own leadership latticework — a diverse, interconnected set of thinking tools you can apply to real leadership challenges.

The latticework grows with every unit:

• Unit 1 — First principles, inversion, second-order thinking, systems thinking, map vs. territory
• Units 2–8 — Each unit introduces domain-specific models (traits, paradoxes, motivation, teams, power, stakeholders, culture)
• Unit 9 — Storytelling as the meta-model for communicating your latticework to others

Your latticework canvas

Use this template to track your growing latticework throughout the course:

Unit	Mental models added	How I applied them
1. Foundations
2. You, the leader
3. Adaptive behavior
4. Environments
5. Team
6. Conflict & Power
7. Stakeholder
8. Context
9. Communication

Keep this canvas as a living document. After each unit, note which mental models were introduced and — more importantly — reflect on how you might apply them. The compound interest of mental models accumulates over time: the more you practice connecting models across domains, the richer your leadership thinking becomes.

Remember Parrish’s three-step approach: Learn (study diverse fields), Apply (use models in real decisions), Reflect (record outcomes and refine).

Transition

Mental models help us think — but who are we as thinkers and leaders?

Next, we look inward at leader characteristics: What traits shape leadership? Where do they come from? And how can self-awareness become a foundation for effective leadership?

Homework

Read Judge et al. (2002) and answer following questions:

• What are the most salient aspects of personality as proposed by the five-factor model of personality (i.e., Big Five traits)?
• How are these traits found to relate to leadership?
• What surprised you most?

Q&A

Literature

Gigerenzer, G., Reb, J., & Luan, S. (2022). Smart heuristics for individuals, teams, and organizations. Annual Review of Organizational Psychology and Organizational Behavior, 9(1), 171–198.

Johnson-Laird, P. N. (1983). Mental models: Towards a cognitive science of language, inference, and consciousness. Harvard University Press.

Jones, N. A., Ross, H., Lynam, T., Perez, P., & Leitch, A. (2011). Mental models: An interdisciplinary synthesis of theory and methods. Ecology and Society, 16(1).

Judge, T. A., Bono, J. E., Ilies, R., & Gerhardt, M. W. (2002). Personality and leadership: A qualitative and quantitative review. Journal of Applied Psychology, 87(4), 765.

Karimi, J., & Walter, Z. (2015). The role of dynamic capabilities in responding to digital disruption: A factor-based study of the newspaper industry. Journal of Management Information Systems, 32(1), 39–81.

Korzybski, A. (1958). Science and sanity: An introduction to non-aristotelian systems and general semantics. Institute of GS.

Kotter, J. P. (2017). What leaders really do. In Leadership perspectives (pp. 7–15). Routledge.

Parrish, S. (2020). The great mental models volume 1: General thinking concepts. Latticework Publishing.

Schreckling, E., & Steiger, C. (2017). Digitalize or drown. Shaping the Digital Enterprise: Trends and Use Cases in Digital Innovation and Transformation, 3–27.

Sullivan, J. (2012). VUCA: The new normal for talent management and workforce planning. Ere. Net, 16.

Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The art and science of prediction. Crown.

Tilson, D., Lyytinen, K., & Sørensen, C. (2010). Research commentary—digital infrastructures: The missing IS research agenda. Information Systems Research, 21(4), 748–759.

Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901.

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144.

VUCA: The new reality. (n.d.). In QRM Institute. https://qrminstitute.org/vuca-the-new-reality

Winston, B. E., & Patterson, K. (2006). An integrative definition of leadership. International Journal of Leadership Studies, 1(2), 6–66.

Footnotes

1. Moore’s law is the observation that the number of transistors in a dense integrated circuit doubles approximately every two years

2. Metcalfe’s law states that the value of a telecommunications network is proportional to the square of the number of connected users of the system

3. Korzybski developed this concept during a period when many fields were grappling with the limits of human understanding - similar to our current AI era.