[2025 KanDDDinsky Keynote] Promises & Pitfalls of the Modeling Path

This is the interactive opening keynote at KanDDDinsky 2025, in Berlin, co-created with Martin Günther and Helen Rapp.

Conference keynotes follow a model. Usually it is the “sage on the stage,” while everyone else does the “sit and get.” The speaker is the cause, the audience the effect.

But what if we tried a different model? What if a keynote became a relational exchange instead of a one-way delivery? A "talk" that does not just inform, but welcomes connection, contribution and inquiry. Where the person in front convenes the experience, and the experience itself does the teaching.

At this year’s KanDDDinsky, we invite you into a learning experiment: a keynote with unlikely content and an equally unlikely format. Together we’ll explore modeling in software and in human systems — in a complex world facing not only technical challenges but also a social and relational crisis.

Of course, this experiment might fail. It might get messy. Skeptics may cross their arms. But isn’t that the very risk of modeling complexity itself?

The question is: what if we did it? And by “we,” we mean all of us. You in?

Transcript

1. Opening Keynote Xin Yao, Helen Rapp, Martin Günther Berlin, October 22, 2025
2. 👉 Choose your seat - follow the rule! object SeatSelection extends App { val tribe = try { // Main path: curiosity and mild social risk findPeople(youDontKnow = true, groupSize = 4) println("✨ You chose adventure: new people, new perspectives! ") } catch { case ComfortableChoice => findPeople(youAlreadyKnow = true, groupSize = 4) println("😌 You chose comfort: familiar faces, safe space. ") } sayHello(tribe) }
3. Connection before Content
4. Promises and Pitfalls of the Modeling Path 4
6. Promises and Pitfalls of the Modeling Path KanDDDinsky 2025, Berlin
7. Why Do We Model?
8. ALL MODELS ARE WRONG, BUT SOME ARE USEFUL. George Box “”
9. All models are wrong because _____________ , But some are useful because _____________ .
10. All models are wrong because _____________ , But some are useful because _____________ . • Representation (of the real “thing”) • Reduction (simplify, amplify, exclude) … • Purpose • Abstraction …
11. Wrongness ⇒ Usefulness (i.e. wrong in the right way) Since all models are wrong the scientist must be alert to what is importantly wrong. It is inappropriate to be concerned about mice when there are tigers abroad. George Box “”
12. Magic Of Abstractions
13. [What is a model in DDD] A system of abstractions What is a Model in DDD? A system of abstractions that describes selected aspects of a domain and can be used to solve problems related to that domain. -Eric Evans
14. A system of abstractions in a Bounded Context By Martin Fowler [Domain Model]
15. [Cognitive Revolution] As far as we know, only Sapiens can talk about entire kinds of entities that they have never seen, touched, or smelled. This ability to speak about fictions is the most unique feature of Sapiens’ language ... Large numbers of strangers can cooperate successfully by believing in common myths. The Cognitive Revolution is accordingly the point when history declared its independence from biology. “” Yuval Noah Harari Abstractions allow human cooperation to scale
16. DDD has its own system of abstractions
17. Promise of Models “Wrong & Useful” Explain Understand Decide Act Cooperate Innovate
18. [Coming up: dense content] Listen for sparks 💫✨🌟💡🤩🤔 Spark 💡 Spark 🤩 Spark 🤔 Aha! Love it! Yes, but… When you catch a spark — a light bulb 💡 or a surprise 😮 — jot it down if it sparks your curiosity. Also notice the ones that spark a bit of resistance 🤔 — let’s catch those too. We’ll revisit the sparks later. Any sticky color will do. 🎨 Spark ❓ How about…
19. Example Of A Model
20. [The Ashby Space Model] The Law of Requisite Variety Variety of Responses High High Low Low Variety of Stimuli 45° Requisite Variety Diagonal Perish Adapt & Survive Variety: a measure of complexity # of distinguishable states in the environment or in the system ~ Ross Ashby (1957) Credit: Ross Ashby, Max Boisot, Bill McKelvey
21. [The Ashby Space Model] The Law of Requisite Variety Variety of Responses High High Low Low Variety of Stimuli 45° Requisite Variety Diagonal Credit: Ross Ashby, Max Boisot, Bill McKelvey The Ordered Regime The Complex Regime The Chaotic Regime
22. [The Ashby Space Model] The Law of Requisite Variety Variety of Responses High High Low Low Variety of Stimuli 45° Requisite Variety Diagonal Credit: Ross Ashby, Max Boisot, Bill McKelvey The Ordered Regime The Complex Regime The Chaotic Regime Adaptive Frontier
23. Use Cases of the Ashby Space Model (Strategic systems design) Analyze problem space complexity Position “Variety of Stimuli” Categorize state variables in Environment <<include>> <<include>> E.g. country, segment, climate, market, opportunity, threat Analyze solution space complexity Position “Variety of Responses” Categorize state variables in System <<include>> <<include>> E.g. product, process, software, technology, org design Strategize
24. [Sample outcome] A strategy process using the Ashby Space Model Variety of Responses High High Low Low Variety of Stimuli 45° Requisite Variety Diagonal Credit: Ross Ashby, Max Boisot, Bill McKelvey The Ordered Regime The Complex Regime The Chaotic Regime Adaptive Frontier
25. Promises and Pitfalls of the Modeling Path
26. Ontology Epistemology ? What is the nature of reality? How do we know? What’s worth knowing? Credit: Andrew J. Wright
27. Domain Observer ? What is the domain complexity? How do we know it? What’s worth knowing? Credit: Andrew J. Wright
28. Where would you place an Emergency Room (ER) domain in the Ashby Space? The Domain Credit: Andrew J. Wright, Harish Jose EMERGENCY ROOM Variety of Responses High High Low Low Variety of Stimuli 45° Requisite Variety Diagonal The Ordered Regime The Complex Regime The Chaotic Regime ? ? ?
29. [Pitfall] Ontological Complexity Realism The Domain EMERGENCY ROOM [Ontological Stance] Complexity exists “out there”, to be discovered and measured. Credit: Harish Jose
30. [Pitfall] Epistemological Representationalism Variety of Responses High High Low Low Variety of Stimuli 45° Requisite Variety Diagonal The Ordered Regime The Complex Regime The Chaotic Regime Adaptive Frontier [Epistemological Stance] Our task to “know” complexity is to create objective & neutral representations of it. The Ashby Space Framework: • Variety of stimuli or responses: objective states • Precise diagonal line: objective measurement • Between regimes: clean boundaries • Modeling outcome: neutral conclusion Credit: Harish Jose
31. [Pitfall] Complexity is immune to the act of modeling Credit: Harish Jose Domain The world out there Me in here Observer / Modeler
32. The Reentry Problem The very act of putting that thermometer into the water, changes the water's temperature, even if you are trying to get an accurate reading. What happens if you use a warm chunky thermometer to measure the temperature of a glass of cold water? Credit: George Spencer-Brown, Harish Jose
33. Epistemic Coupling Observer / Modeler Observed / Modeled Our modeling shapes the reality we try to understand The reality we “see” changes us (knowledge, action, belief) Epistemic coupling: The recursive interaction between knowing systems and their environments. ~ Harish Jose Epistemic Coupling
34. See it at work in Ashby Space Variety of Responses High High Low Low Variety of Stimuli 45° Requisite Variety Diagonal The Ordered Regime The Complex Regime The Chaotic Regime ? ? ? Recursion: The very act of modeling/strategizing the domain makes “it” more complex (the observer-system relations), and that in turn makes “us” more complex as observers. Reentry: The org that is modeled does the modeling (i.e. choosing the abstractions, relations, etc.)
35. [Reflect & Connect] 💭🌿💬 What’s alive for you at the moment? Turn to a neighbor. Take turns to share one of: • “One thing that struck me is _______ ”. • “One thing that surprised me is _______”. • “One thing I’m curious about is _______”. No need to solve — just listen to each other.
36. Problem Space & Solution Space are artificial divisions Analyze problem space complexity Position “Variety of Stimuli” Categorize state variables in Environment <<include>> <<include>> E.g. country, segment, climate, market, opportunity, threat Analyze solution space complexity Position “Variety of Response” Categorize state variables in System <<include>> <<include>> E.g. product, process, software, technology, org design
37. Discovery & Design happen at the same time (Objective discovery is an illusion) • There is no problem space “out there” waiting to be discovered. • Distinctions are designed by observers, not discovered in objective reality. • Likewise, there is no solution space “in here” waiting to be catalogued. • There is only the ongoing transaction through which the modeler and the environment mutually specify each other. Credit: John Dewey, Harish Jose
38. Ubiquitous Language is at best asymptotic (Great minds don’t think alike, or speak alike) Credit: Alex Morgadas The business mind The developer mind
39. No accurate, objective causal loops in social systems
40. Our favorite models can trap us ALL MODELS ARE WRONG, BUT MINE IS BETTER. “”
41. Epistemic recursion - it’s Groundhog time! Observer / Modeler Observed / Modeled Epistemic Coupling 1a. Apply models & methods 1b. Sort of works, but… 2a. Improve model & practice 2b. It works even better now 3a. Expertise, talks, blogs 3b. Likes & Follows 4a. Let me enable you. 4b. Doubt, Dissent, Resistance 5a. Handle resistance
42. Stuck on the rung? “OMG, How did it happen?”
43. Fiction → Myths → Beliefs → Identity Observer / Modeler Observed / Modeled Our modeling shapes the reality we try to understand The reality we “see” changes us (knowledge, action, belief) Epistemic Coupling
44. What do you see
45. How do you unsee it, and do you want to?
46. [Wittgenstein’s Ladder] We need tools we must abandon My propositions serve as elucidations in the following way: anyone who understands me eventually recognizes them as nonsensical, when he has used them – as steps – to climb beyond them. He must, so to speak, throw away the ladder after he has climbed up it. “” Ludwig Wittgenstein
47. [Substractive Modeling] Pull away our “learning aids” even if it hurts
48. Who can take a shot for DDD? I like to believe that DDD experts know this very well, and view any DDD material as a starting point, not an end result – but if all you’re doing is applying the by-the-book definition of existing DDD terms, and trying to shoehorn any problem into this existing structure, yours is a very sad designer’s life. There is a life beyond DDD. Not every good design needs to be Domain-Driven (though I can accept it should always be driven by the domain, just not necessarily in the DDD sense). You can design good systems even if you’re not a DDD expert. “” Stefan Tilkov
49. Epistemic Humility In our community or your work context, • Which of your favorite ladders do you find hard to step off, or hold lightly? • Which ones sometimes get turned into fences - or even weapons? “It’s hard to learn if you already know”
50. Closing, but …
51. What comes after the ladder? Ditch our tools, models and methods? INTELLECTUAL VOID?
52. … … Sorry, an open ending
53. One next exploration …
54. Commitment Openness # understand deeply # decide wisely # act firmly # collaborate effectively # hold lightly # model how we model # listen deeply # model attention Systems Thinking Systems Being
55. Quality attributes in sociotechnical architecture Connectedness Aliveness Belonging Freedom Wholeness Modularity Scalability Security Elasticity Reliability Social Architecture Technical Architecture Openness Usability
56. The Declaration of Interdependence (Social System → Community) Connectedness Aliveness Belonging Freedom Wholeness Modularity Scalability Security Elasticity Reliability Social Architecture Technical Architecture Openness Usability
57. Connection through Content
58. Get the app
59. Thank you!