The Red Ball Method
A High-Velocity, Question-Driven Innovation Protocol for Interdisciplinary Teams
Author: Gunnar Øyvin Jystad Fredrikson
Version: Draft for professional and academic review
Abstract
The Red Ball Method is a high-velocity innovation protocol designed to compress both effort-time and calendar-time from problem framing to minimum-value delivery. Unlike many innovation approaches that prioritize structured ideation or controlled failure, the Red Ball Method optimizes for rapid forward momentum by combining interdisciplinary equality, question-driven exploration, and immediate build-to-answer cycles.
Rather than treating convergence as the primary objective, the method treats convergence as a byproduct of accelerated insight generation and minimum value delivery. It can operate independently or function as a protocol embedded within larger frameworks such as Design Thinking, Dual Track Agile, and Lean Startup.
This document formalizes the method, articulates its positioning and novelty, defines boundary conditions, and proposes falsifiable hypotheses for future empirical validation.
1. Positioning and Core Thesis
Most innovation frameworks optimize for one of three things:
- Quality of exploration
- Quality of validation
- Quality of execution
The Red Ball Method optimizes for velocity toward usable value, without structurally sacrificing interdisciplinary equality or learning.
Its central thesis:
Innovation velocity increases when interdisciplinary teams are allowed to own questions, explore them in parallel, and build minimum value artefacts that answer those questions while delivering real value.
Velocity is defined along two dimensions:
- Effort velocity — minimizing unnecessary meetings, permission loops, and idea filtering
- Calendar velocity — minimizing time to something real that stakeholders can use
2. The Shift: From Idea-Led to Question-Led Innovation
Traditional brainstorming is idea-led.
The Red Ball Method is question-led.
A Red Ball is not merely an idea. It is:
A question with enough shape to explore through action, and enough potential to become a minimum value product.
This shift has structural implications:
- Questions reduce ego attachment
- Questions invite exploration rather than defense
- Questions expose unknowns explicitly
- Questions enable build-to-answer cycles
Where some methods promote “fail fast,” the Red Ball Method promotes:
Advance fast toward anything that can work.
Failure is not eliminated. It is de-prioritized in favour of credible forward motion.
3. Core Design Principles
3.1 Velocity as a Primary Metric
The method explicitly optimizes:
- Time from question to build
- Time from build to stakeholder exposure
- Total effort spent per iteration
3.2 Distributed Ownership as Structural Rule
Every participant:
- Selects one Red Ball
- May adapt or reframe any ball
- Cannot cancel another’s ball — only add to it
Equality is designed, not assumed.
3.3 Interdisciplinary Team as Latent Toolbox
Teams are treated as dynamic toolboxes.
The full capability of the team is not known at the outset.
It reveals itself through exploration.
This is a critical departure from role-based models.
3.4 Minimum Value Product (MVP 2.0)
A Minimum Value Product must:
- Be usable by a real stakeholder
- Deliver measurable value
- Answer at least one key innovation question
- Enable forward movement
This differs from many MVP interpretations that focus solely on hypothesis testing.
4. The Red Ball Velocity Loop
Step 1 — Assemble a Small Interdisciplinary Team
Maximum seven including facilitator.
Step 2 — Frame the Strategic Question Space
Define what we know, what we do not know, and what must become true.
Step 3 — Generate Question-Balls
Each participant selects one question worth pursuing.
Step 4 — Parallel Exploration
Participants explore independently through rapid builds, reframing, and critique.
Step 5 — Build-to-Answer
Each ball must produce something tangible.
Step 6 — Momentum Selection
The team selects the ball with the strongest forward momentum.
Step 7 — Minimum Value Delivery
The selected ball becomes a usable artefact.
Step 8 — Continue or Release
Evidence determines scaling, adaptation, or archive.
Convergence is present, but velocity is the driver.
5. Embedding Inside Other Frameworks
The Red Ball Method is best understood as a high-velocity upstream protocol.
Inside Design Thinking
Design Thinking emphasizes empathy, problem framing, and iterative prototyping.
Red Ball can replace or compress:
- Early ideation workshops
- Extended brainstorming phases
It accelerates the move from empathy insights to tangible artefacts.
Inside Dual Track Agile
Dual Track separates discovery and delivery.
Red Ball can serve as:
- The discovery engine within the discovery track
- A bridge between discovery and backlog-ready delivery
Inside Lean Startup
Lean assumes hypotheses are clear.
Red Ball operates before hypotheses are stable.
It generates testable hypotheses through action rather than speculation.
6. Comparison with Adjacent Frameworks
| Framework | Primary Focus | Speed Orientation | Ownership Structure | Question-Driven? | Minimum Value Focus? | Red Ball Differentiator |
| Design Thinking | Empathy & divergence | Moderate | Collective | Partially | Iterative | Faster divergence-to-build |
| Google Sprint | Time-boxed validation | High (fixed) | Centralized decision | Limited | Prototype validation | More flexible, parallel ownership |
| Lean Startup | Hypothesis testing | Iterative | Founder-led | Yes | MVP | Operates before hypotheses stabilize |
| Agile/Scrum | Delivery | Iterative | Role-based | No | Incremental delivery | Upstream velocity engine |
| Dual Track Agile | Continuous discovery | Continuous | Mixed | Yes | Backlog ready | Stronger parallel exploration |
| Lean UX | Collaborative UX | Moderate | Shared | Partially | Prototype feedback | Structured equality rule |
| Double Diamond | Divergence/convergence | Moderate | Phase-based | Partially | Not explicit | Velocity prioritization |
| Jobs To Be Done | Customer need framing | Slow/moderate | Analytical | Yes | Not build-centric | Build-to-answer emphasis |
| TRIZ | Structured problem solving | Analytical | Expert-driven | Yes | No | Hands-on interdisciplinary builds |
| Effectuation | Entrepreneurial logic | Adaptive | Founder-centric | Yes | Action-based | Formalized team equality |
| Stage-Gate | Governance control | Slow | Hierarchical | No | Formal validation | Radically higher velocity |
| Continuous Discovery | Ongoing testing | Continuous | Product-led | Yes | Yes | Explicit parallel ownership |
| Cynefin-based experimentation | Context classification | Contextual | Expert-led | Yes | Varies | Less analytical, more build-driven |
| Theory of Constraints (innovation flow) | Bottleneck removal | Flow-based | System-level | No | No | Micro-level question velocity |
7. Strengths Emerging from This Reframing
- Structural velocity as measurable outcome
- Distributed agency embedded in process rules
- Question-driven exploration reduces ego defence
- Build-first mentality compresses theory-to-practice gap
- Flexible insertion into other frameworks
8. Boundary Conditions and Safety Constraints
The method is not ideal when:
- Regulatory validation must precede exploration
- Strategic direction is fully defined and stable
- Hierarchical decision control cannot be relaxed
- Psychological safety cannot be reasonably established
Safety rule:
Ideas violating legal, ethical, or safety standards are filtered before exploration.
9. Falsifiable Hypotheses for Validation
H1: Teams using parallel question-balls will reach usable artefacts faster than teams using serial ideation.
H2: Explicit distributed ownership increases engagement and iteration throughput.
H3: Question-led framing reduces defensive behaviour compared to idea-led brainstorming.
H4: Minimum Value Products produced under velocity constraints will produce comparable learning outcomes to traditional MVP cycles but in shorter time.
10. Is This Novel or a Remix?
The Red Ball Method is not a rejection of existing frameworks.
It is a recombination with a different center of gravity.
Its novel contribution lies in:
- Making velocity the primary design constraint
- Treating distributed ownership as structural rule
- Replacing brainstorming with question-driven build cycles
- Formalizing minimum value delivery as both learning and value instrument
- Operating as a bridge protocol across frameworks
If future validation shows it is merely a variant of existing discovery practices, it may be best positioned as:
The Red Ball Protocol: A velocity loop for interdisciplinary innovation teams.
Closing Statement
The Red Ball Method is an attempt to formalize a practice observed across multiple innovation contexts over a decade: interdisciplinary teams move fastest when they are equal, question-driven, and building immediately toward value.
This document is an invitation for critique, testing, and refinement.
Beyond Competitive Advantage: Strategic Continuity in the Infinite Business Landscape
Modern strategy literature is saturated with the language of advantage: competitive advantage, first-mover advantage, sustainable advantage. Yet the historical record suggests something more sobering: advantages erode.
Technologies decay.
Markets reorganize.
Institutions mutate.
From a Darwinian perspective, survival is not secured by dominance, but by adaptation. From a thermodynamic perspective, systems naturally move toward entropy unless energy is continuously applied. From a game-theoretic perspective, equilibrium is rarely permanent. If we accept that business operates in a dynamic, non-equilibrium environment, then perhaps the central strategic task is not to win — but to remain viable.
This article proposes a structured model for what I call strategic continuity: the capacity of an organization to survive the death of its own services while remaining coherent in identity and direction.
The Infinite Game as Structural Premise
The distinction between finite and infinite games, articulated in philosophy and later applied to organizational theory, offers a useful lens.
• A finite game has known players, fixed rules, and a defined endpoint.
• An infinite game has shifting participants, evolving rules, and no terminal victory condition.
Most individual services are finite games. They launch, compete, generate returns, and eventually decline. But the organization itself participates in an infinite game. There is no final win-state. There is only continued participation or exit.
Game theory formalized by John Nash demonstrates that equilibrium conditions depend on player expectations and strategic interaction. In real markets, players continuously enter and exit, altering the payoff matrix. Stability is provisional.
From evolutionary biology to dynamic systems theory, the pattern repeats: systems that survive are those capable of adaptation under changing constraints. The question therefore becomes:
How do we design organizations structurally capable of continuity in an infinite landscape?
The Infinite Service Continuity Model
To answer this, I propose a layered model. Imagine the organization as a structured system composed of four concentric layers.
- Infinite Vision (Core Identity)
At the center lies Infinite Vision: purpose, identity lock points, moral anchors, and sustainability commitments. In philosophy, identity persists through change if core properties remain stable. In constitutional law, enduring principles anchor evolving interpretation. In finance, long-term value presumes consistent underlying thesis. Vision must survive product cycles. If a company’s identity collapses when a service fails, the service was mistaken for the organization. - Endless Strategy (Governance and Continuity Logic)
Surrounding the core is Endless Strategy: governance structures, cultural norms, legal compliance, and value systems. Ronald Coase demonstrated that firms exist to reduce transaction costs within markets. Governance, therefore, is not administrative overhead; it is structural logic. In legal theory, durable institutions balance flexibility with rule-based constraint. Endless Strategy defines how the organization moves without dissolving its identity. It is the stabilizing field around the core. - The Time Spectrum (Strategic Awareness Across Horizons)
Strategy is often collapsed into annual plans. Yet time itself is multi-layered.
The Time Spectrum includes:
• Reflective – Institutional memory and post-mortem learning
• Actual – Present operations
• Focus – Immediate directional prioritization
• Tomorrow – Forecast based on available evidence
• Future – Scenario exploration beyond current models
• Endless – Long-arc existential positioning
Physics reminds us that systems are path-dependent. Economics shows that expectations shape behavior. Psychology, particularly Daniel Kahneman’s work on cognitive bias, demonstrates that short-term focus often overrides long-term reasoning.
The Time Spectrum corrects for temporal blindness by institutionalizing multiple horizons simultaneously. - Finite Services (Orbiting Activations)
Beyond the strategic ring are finite services. Each service is an activation:
• It has a birth.
• It generates value.
• It accumulates entropy.
• It eventually declines.
Joseph Schumpeter’s concept of creative destruction captures this dynamic: innovation dismantles previous structures. In finance, portfolio theory (Markowitz) spreads risk precisely because individual assets are volatile. Services are not identity. They are portfolio elements. An organization that understands this can allow services to die without existential panic. - The Ecosystem Layer (Experience and Data)
Surrounding everything is the ecosystem:
People.
Processes.
Market structures.
Regulation.
Technology.
Data.
Claude Shannon’s information theory reminds us that data is not meaning, it is signal. Meaning arises through interpretation. Data precedes technology. It may exist as archival documents, lived experience, or mathematical models. Technology, including artificial intelligence, is a retrieval and transformation mechanism.
Within this model, AI systems – including systems that simulate “personalities” – function as advanced tools for engaging with data. They are not identities. They are structured interfaces for navigating complexity, enabling interdisciplinary teams to interrogate archives, simulate scenarios, and detect patterns at scale. Used correctly, AI becomes a cognitive amplifier within the ecosystem layer. Used incorrectly, it becomes a distraction from structural design.
Finite Innovation Inside Infinite Structure
Continuity requires renewal. High-velocity innovation frameworks for rapid convergence and scalable outcomes operationalizes innovation as a finite, repeatable cycle within the infinite structure. Small interdisciplinary teams pursue ideas in parallel. Psychological ownership is given and preserved to ensure process momentum. Convergence is structured. Key concepts advance to scalable testing; others remain archived.
This resembles scientific hypothesis testing: multiple models compete, one survives provisional validation, but none claim final truth. Thomas Kuhn’s work on paradigm shifts reminds us that even dominant frameworks eventually yield. Innovation methods that allows organizations to generate new service orbits continuously, at low cost and high velocity, without destabilizing the core are critical to keep evolving and adapting into an ever changing business ecosystem.
Cross-Disciplinary Foundations
This model can draw implicitly from multiple enduring fields such as:
• Natural Sciences: Evolutionary adaptation and entropy.
• Mathematics: Game theory and equilibrium instability.
• Psychology: Bounded rationality and cognitive bias.
• Finance: Portfolio diversification and risk distribution.
• Law: Institutional continuity through rule structures.
• Philosophy: Identity persistence through structured change.
• Marketing: Brand coherence amid tactical variability.
None of these fields alone explain continuity. Together, they converge on one insight:
Stable systems are those that separate core identity from adaptive components.
Who This Framework Serves
This architecture is not optimized for early-stage startups struggling for viability. In the earliest phase, focus must dominate. The Infinite Service Continuity Model becomes relevant when an organization already occupies space in the landscape – when it is already playing the game.
It is particularly relevant for:
• Established technology firms navigating platform transitions
• Media institutions adapting to digital ecosystems
• Public agencies balancing regulation and innovation
• Mature organizations managing multiple service portfolios
These entities face a different problem than survival through growth. They face survival through transformation.
Beyond Advantage
Competitive advantage is a finite concept. Strategic continuity is infinite.
Advantage may secure temporary dominance. Continuity secures participation across generations of services, technologies, and market structures. In the infinite business landscape, the objective is not to defeat competitors permanently. It is to design an organization capable of replacing itself, repeatedly, without losing itself. The only true failure is structural inability to adapt. And that, unlike market fluctuations, is a choice.
Functional Profiling and the Design of AI Personalities
A Framework for Coherent, Trustworthy and Purpose Driven Artificial Agents
Author:
Gunnar Øyvin Jystad Fredrikson
Service Designer and AI Strategy Practitioner
Date:
2026
Abstract
As artificial intelligence transforms from a computational tool into an interactive partner, organizations face an emerging design and governance challenge. Users increasingly perceive AI systems as social actors, yet few organizations have roles or frameworks dedicated to the intentional design of the agent’s identity, behaviour, and long term consistency. This paper introduces the concept of functional profiling as a method for defining the stable behavioural characteristics of AI agents. It draws on research in psychology, human computer interaction, game design and AI ethics to propose a structured model for creating, monitoring and evolving personality driven agents in a responsible way.
1. Introduction
Artificial intelligence is shifting from a backend capability to a frontstage participant in human workflows. Large language models and multi agent systems now exhibit behaviours that humans interpret through familiar psychological lenses. Research consistently demonstrates that people apply social, emotional and moral expectations to interactive systems, even when they are fully aware that these systems are artificial.
Nass and Moon describe this phenomenon as the Media Equation, showing that individuals respond to computers using the same social rules and expectations they apply to humans (Nass and Moon 2000). As AI becomes more conversational and adaptive, this effect increases rather than decreases.
Yet most organizations design AI systems as though these social expectations are irrelevant. Engineers optimise performance. Designers shape prompts. Product managers define features. Legal teams review compliance. No one is explicitly responsible for the personality, behavioural integrity, or long term consistency of the agent.
This gap introduces organisational and ethical risks. When a system changes tone abruptly, shifts roles, contradicts earlier statements, or behaves unpredictably, users lose trust. In regulated industries, inconsistent agent behaviour can have serious consequences.
To address this emerging need, this paper introduces the concept of functional profiling as a systematic and responsible method for designing the personality and behavioural structure of artificial agents.
2. Theoretical Foundations
2.1 Personality as a Functional Construct
In psychology, personality refers to a stable set of dispositions that predict behaviour across contexts. The Big Five (or OCEAN) model is a widely researched framework describing these mechanisms (McCrae and Costa 1996). When adapted for AI systems, these traits can become functional parameters rather than emotional states.
2.2 AI as Social Actor
Human computer interaction studies show that humans consistently attribute intent, emotion and morality to machines that exhibit social cues. This is not a misunderstanding but a form of cognitive shorthand. People treat socially present AI systems as relational partners.
This is central to understanding why AI personality design matters. Predictability and coherence are not aesthetic touches but essential components of user trust.
2.3 Behavioural Consistency in Agentic AI
Emerging research on personality traits in large language models shows that these models display stable behavioural signatures that can be measured and influenced (Serapio García et al. 2023). As multi agent systems become more complex, the need for clear behavioural boundaries increases. Without them, agents may drift, converge, or diverge in ways that are difficult to predict or explain.
3. The Concept of Functional Profiling
Functional profiling is the structured design of an AI agent’s stable behavioural characteristics, role boundaries, memory systems and interaction patterns. It does not attempt to imitate real humans. Instead, it defines artificial identity through purpose, constraints and transparency.
Working definition:
Functional profiling is the intentional design of an AI agent’s dispositional behaviour, memory scope and interaction style according to function, context and ethical boundaries.
The objective is a coherent agent that behaves according to stable internal principles and remains aligned with organisational goals and user expectations.
4. Methods for Creating AI Personalities
The following five methods integrate insights from psychology, user experience, service design, game design and AI governance.
4.1 Role Constrained Behavioural Profiling
This method defines the agent’s role as the anchor for acceptable behaviour. The agent’s tone, risk posture and decision boundaries are derived from its purpose.
Applications: healthcare triage assistants, financial advisors, public sector agents.
4.2 Trait Based Psychometric Profiling
This adapts psychological trait models into computational parameters. For example, openness becomes exploration bias, while agreeableness becomes conflict management behaviour.
Applications: coaching systems, advisory tools, collaborative agents.
4.3 Character Sheet Modeling
Borrowed from game design, this method creates a transparent and auditable record of the agent’s identity, including strengths, weaknesses, locked attributes and permitted evolution paths.
Applications: multi agent systems, research environments, creative tools.
4.4 Brand and Voice Aligned Profiling
This aligns the agent’s behaviour with organisational values. This extends beyond tone to include confidence levels, escalation paths and refusal strategies.
Applications: customer interaction systems, media platforms, commerce.
4.5 Ethically Bounded Adaptive Profiling
This allows the agent to evolve behaviour in a controlled manner while respecting ethical and legal constraints. Drift monitoring and explainability requirements are central features.
Applications: long lived agents, personal assistants, enterprise AI.
5. Governance and Ethical Considerations
AI personalities raise questions beyond design, including accountability, consent, privacy and transparency. The closer an agent resembles a human conversational partner, the greater the obligation to clarify its intent and limits.
Key governance questions include:
- What does the agent remember and for how long
- How is personality drift detected and managed
- Who is accountable for the behaviour of the agent
- Can users inspect or understand the agent’s identity model
Failure in any of these areas creates risks for organisations and users alike.
6. Strategic Implications for Organisations
Organisations that invest early in functional profiling gain advantages in trust, differentiation and regulatory preparedness. As AI becomes a central part of human facing services, behavioural integrity will matter as much as technical performance.
This creates a demand for new roles, including AI experience strategists, agent identity designers and behavioural architects. These roles are interdisciplinary by nature and require competencies that do not map cleanly onto existing job titles.
7. Conclusion
AI is crossing a threshold where it is no longer sufficient to design interfaces alone. We are now designing identities. Functional profiling offers a structured approach that draws from established academic disciplines while addressing emerging strategic needs. It supports the creation of agents that are coherent, predictable and ethically grounded.
The question is no longer whether AI should have personality, but how that personality is designed and managed.
References
McCrae R and Costa P (1996). Toward a new generation of personality theories. Journal of Personality 64(1).
https://www.researchgate.net/profile/Paul-Costa/publication/242351438_Toward_a_new_generation_of_personality_theories_theoretical_contexts_for_the_Five-Factor_Model/links/54ebdbd50cf2ff89649e9f57/Toward-a-new-generation-of-personality-theories-theoretical-contexts-for-the-Five-Factor-Model.pdf
Nass C and Moon Y (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues 56(1).
https://doi.org/10.1111/0022-4537.00153
Serapio García G et al. (2023). Personality Traits in Large Language Models. arXiv.
https://arxiv.org/abs/2307.00184
Framework
This page contains an quick overview of my gamification design framework.
- Point of entry (PoE)
- User profile
- Collaboration artifact
- Game mechanics and collaborative interactions
- Communication and coordination
- Rewards
- Player experience
Point of entry (PoE)
Point of entry is how the player ‘enters’ the game as well as the ‘location’ of the gamified solution and/or game mechanics. It is also a combination of technology, platform and network that all influence how a player will be able to interact with the gamified solution itself as well as the other players.
User profile
The player profile is anchored in a real person, and requires authenticated data when created. Once created the player can personalize the profile by filling out the different parts of it. As the player progresses more options will be available for configuration as well as more data from the application will be used to present the player for other players.
Collaboration artifact
The focus of this framework is to motivate and enable players to collaborate. For such collaboration to work the players will have to enter into collaborative ownership of a game artifact. This collaboration artifact will contain all of the data and track the progress and development of the player’s collaborative efforts and track how the players interact with it. As an artifact grows/evolves in the game it is how the players are able to achieve, master and interact with the game and also how they receive their intrinsic and extrinsic rewards.
Game mechanics and collaborative interactions
The core mechanics of the game are the creation, development and/or interaction with collaborative artifacts. After having created an artifact the player will have access to game mechanics for interacting with the artifact and to collaborate with other players to enhance or develop the artifact. As a player you can also initiate interest in other players artifacts and be given access to interacting with these.
Communication and coordination
Tools for communicating and coordinating collaborative efforts are critical parts of the framework. In addition to designing the tools themselves they also need to be integrated to enhance and assist players to interact and collaborate. Good tools for communication and coordination will help create and develop collaboration artifacts as well as build a solid social platform for the players.
Rewards
For basic gamification the use of rewards is a common game mechanic, but when introducing large scale collaborative play they way rewards are used will change when they are no longer given to a single player in direct relation with that players choice or performance in the game. It is important to design rewards that can belong to (owned by) a group of collaborating players and that has an extended life cycle to reflect the time and effort the players invest in their collaborative artifacts.
Player experience
A collaborative game experience has a different appeal to its players than other types of games. If we consider player types as traits all players have more or less of, we are more likely to find certain traits connected with certain game interactions. Deciding on which player types and /or traits the solution will work with creates a solid foundation for building a player experience that will be able to motivate a broad audience. Secondly the players will have different experiences based on where they are in their player journey, and ensuring quality player experiences exists for each stage of this journey is important.
There are three important visual models that are a part of the framework;
- Point of entry: showing both the where the gamified solution exists in relation to other parts of the system/service as well as how the hardware and network architecture looks are good ways to look at a system.
- The basic framework; point of entry, user profile, collaborative artifacts, game mechanics and collaborative interactions including communication and coordination.
- A three dimensional ‘check-list’ covering player types, player journey and the three parts that make up collaboration; cooperation, co-creation and coordination.
These three models will be a map of how to create a collaborative gamification application.
Expanding the sandbox?
From Azeroth and onward the World of Warcraft has expanded and grown. The virtual world (or open world/’sandbox’) has gone from its two initial continents to now include several and with more on the way. It combines an open environment for its players to explore and interact with, but at the same time it also represents the limitations that the game presents us with. Exploring is a natural part of the game, and most players will at some point try to find their way to places that seem to be out of reach or difficult to reach. To some it is a challenge in itself to find ways to discover areas in the game that are difficult to reach, but so far no ‘new dimensions’ have been added to game.
If we look at another MMORPG like Neverwinter we find that they have added tools for the players to ‘add’ content and expand ‘the sandbox’, and then letting other players rate the experience. And one could say that WoW lets its players introduce some such additions through ‘addOns’ that lets the players track game data as well as create and adjust the user interface of the game. With the number of such ‘addOns’ numbering in the 1000s it is clear that this one of the areas where the WoW players are ‘innovating’ their game. Players also interact in other channels (official forums, guild sites and so on), but it is through the ‘addOns’ that they create interfaces for player interactions within the game itself. And when it comes to collaborating this is the only area players are given a limited level of freedom to innovate. And looking at the number of such ‘addOn’ projects and how many of them involve more than one developer it is main area for player innovation.
Now why is this interesting? It shows that players innovate. But it also shows that innovation is exclusive for those willing and able to invest time and effort into developing ‘addOns’. What I would like to see is a lowering of the threshold for having players innovate as part of the game beyond the ‘addOns’. Make it easier to create ideas, come with suggestions, rate suggestions and actually influence how the game evolves from a game experience perspective. It looks as if Blizzard both enjoys a close relationship with its gamers and that they want to be able to communicate with their players, but at the same time they have not developed any integrated services for this type of interaction.
If I return to Neverwinter and their solution for adding content to their game I really enjoy this concept, but alas it also felt like it failed on achieving what it set out to do. The solution felt a bit too simple and the created content all to often felt ‘cut off’ from the game and with little or no direct in-game connection. To a certain extent it felt like this part of the game was ‘under construction’ on a permanent basis and that everything within would have an ‘un-finished’ feel to it. Which felt sad as I really loved the concept and idea of having your players add both content and expand on your game world. So how does one continue to improve on such player generated content? I think that most gamers today that invest time in MMORPGs would love to be involved its development if it was made simple enough and easily accessible, and I hope to see this part of such game improve. Large games will always be a collaborative effort where the players are just as much a part of the creation as the actual producers and developers.
Long quest or short?
From the 5 minute Daily-quest to the several weeks long Legendary Cloak quest-line. Having spent multiple hours in World of Warcraft these last few months I can readily say that I have been doing my share of both, and I have found a few elements that I have been experiencing as hindrances rather than entertaining.
Lets start with Daily-quests as these are often how a gaming session would start out, and all in all I do not mind pursuing a few of these to earn some gold, harvest some reputation and scrape together some crafting goods along the way. Saying hi to friends that are online and checking around for events or activities for the evening. Its a good way to kick off the game session, but there are a few drawbacks. First off its the pure number of such quests and elements of the game that are considered obligatory to be able to enter into the more challenging parts of the end game (game activities after hitting the maximum attainable level in the game). Second its the feeling of work where it stops being ‘blissful productivity’ and become repetitive ‘waste’ of time I would have rather spent doing something more fun. My solution in the end has been to pick up a few very quick such quests along with a very limited amount of those that are considered ‘obligatory’ (often needed to be able to enjoy other parts of the game or as parts of longer quest-lines). Problem in my situation is progress as completing important combinations or sequences of such quests takes me much longer than someone spending most of their time online or spending more time grinding their way through these quests in larger scale.
Next is the longer quest-lines that require weeks of work to complete. I mostly enjoy this method of storytelling with one important hindrance. Game play to me is more like a good movie or a great book and less like an ongoing TV-series, so when I am forced to ‘put away the book’ or hit ‘pause’ on my movie I become really annoyed. There are presently two types of game mechanics like this where one is connected to item drops and the other to a virtual currency where there is a limit to how much of this currency you are allowed to earn every week. Going through end game instances hunting for these drops also earns me the possibility for item upgrades and is part of the end game I would be pursuing anyway. So this version of blocking my story I can live with. Its the virtual currency I have trouble with. To fill my weekly quota it is not enough to go through the end game raid instances, you also need to push through a number of Daily-quests and/or heroic dungeons/scenarios. So I grind my way through to reach the cap only to have to repeat the same grind the following week and then again the week after. It is not the first time this type of game mechanics have been introduced into World of Warcraft, but I really hope they can find better and more relevant game activities for us to pursue when working our way through some of the best end game story-lines. Not to mention the feeling of having completed an epic achievement when completing them.
Finally; do not mix PvE and PvP. These are two completely different types of game play and forcing non-PvP gamers to fail their way through numerous PvP-grinds facing massively superior and motivated PvP-players destroys my evening. Many PvE-players might enjoy some PvP to add diversity to the game, but its by choice. I found the solution for the Throne of Thunder where the players could chose to earn their reputation and progress through either PvE- or PvP-quests perfect, but being forced to do PvP as part of the Legendary quest-line was a massive game destroyer for me an I would end up dreading to have to go online to play at all.
Behavior in raids – progressing or regressing as a group
There are two side to this take on group dynamics, one positive and one negative. One set the stage for increased performance of the group members. As the result of internal competition and performance tracking or just that the presence of others help facilitate their own ability to perform. The other involves ‘hiding in plain sight’ while letting the rest of the team do the job. Positive tracking seems to be the key to make sure that all pull their weight with emphasis on the positive. Its ok to have a bad day, just not every day there is a raid planned.
Personally I subscribe to the Pareto Principle (80/20) for analyzing raid performances, and in more ways than one. Lets look at some examples of how this might be useful;
1. No matter how awesome your raid group is you will always have a someone that is having a less than perfect day. Or you can turn it around and saying that there will always be a few that are having a great day and performing above and beyond what they normally would. No matter which version of the Pareto Principle you make use of its all about expecting the balance of your group to be different from raid to raid. The number one DPS might always top the DPS trackers, but gap to the number two is likely to be different, and sometimes this has nothing to do with the game itself. Being able to identify variations like this will help you adjust strategies accordingly.
2. When given the choice for multiple strategies it is always difficult to decide if one should spend five wipes on the most promising of them or try out five different strategies and see how they pan out and then chose the one that worked best. Or test two strategies two times. There are three parts to this way of deciding on strategies. First is researching and analyzing different strategies, both the ones you can find online and those you draw up yourself. Second is about knowing your team and understanding which of these strategies are likely to work or fail. And finally how many wipes before you change strategy. The combination of these will ensure that wiping does not feel like you are running constantly into a wall hoping it will fall over due to some miracle of random luck…
3. The final example that I find important to mention is when everything is going wrong. Often when things seem to be failing all over the place there are a very few reasons for it; 20% of the errors being made are resulting in 80% of what is going wrong. The trick is to identify and fixing those key errors or if this is not an option its time to change the chosen strategy itself.
In an earlier post we touched on the topic of conformation in groups, and so far it seems that high performance creates better players as much as a low performance results in massive slacking and crappy performances. I have to admit to being a ‘victim’ of both; if a raid has set my mood on a negative curve I am more likely to be counter productive and at the same time I see that high performance encourages me to push myself harder and increase my game awareness.
So to make some conclusions; raiding is rarely WYSIWYG, and as much as there are no real clear black and white there is no pure gray either. The Pareto Principle mainly tries to focus on the fact that there is always a balance, but that it is rarely 1:1. Some things you can track with addOns, some can be tracked by just knowing your players, but some things are left open to pure intuition or even clairvoyance if you are a believer in such. And the more present something good or bad is in any given raid the more likely it is to breed more of the same.
Gunnar Øyvin Fredrikson 