Nathaniel Cooke

Last year I teamed up with Georgia Bullen, Executive Director of Superbloom Design, to offer the cohort a session on Funding 101 for Civic & Political Tech. We covered a lot of ground in just an hour, including candid takes on the funding landscape, entity and legal eligibility types, and winning strategies. Now I work for a foundation, so hopefully it'll be even better!

A casual, ask-the-expert session on how projects actually get funded in the real world with Georgia Bullen and Matt Stempeck.

​If you’ve ever thought “I have an idea… now what?”, this is for you.

​We’ll discuss the practical side of getting a project off the ground: where funding comes from, how to find opportunities, what early-stage teams should prioritize, and what makes an application stand out when you don’t yet have a track record.

​This session is especially relevant for fellow candidates thinking about what happens after a prototype: turning something scrappy into something sustainable.

​Topics we’ll likely touch on:

• ​Where to find funders and active opportunities

• ​What first-time projects should look for (and avoid)

• ​Legal structures and setup basics

• ​Signals of credibility when you’re unknown

• ​Partner development and early traction

• ​What strong grant applications actually look like

​Georgia is the Executive Director of Superbloom, where she works at the intersection of technology, movements, and public interest infrastructure, supporting teams building tools for civic participation, accountability, and collective action. Her work spans fundraising strategy, ecosystem building, and helping early-stage projects navigate the path from idea to impact.

​Matt Stempeck is a civic technologist and researcher focused on how technology can strengthen democracy and support real-world civic action. He is the founder of the Civic Tech Field Guide, a global collection of tools and organizations working in the public interest. He is faculty at Newspeak House (the London College of Political Technology) and has held roles across government, academia, and industry, including at Microsoft and Cornell Tech. His work focuses on helping projects move from ideas to impact, with an emphasis on practical strategies for participation, funding, and movement-building.

Collective intelligence as governance of AI, and AI for collective intelligence.

This topic covers the distinction between democracy and collective intelligence, why you might choose one over the other, and where they trade off. How does human CI and artificial CI intersect? What can societies of humans and agents can produce? What are the open questions?

Some examples

• Democratic Inputs to AI - CIP & Open AI
• Collective Constitutional AI: Aligning a language model with public input - CIP & Anthropic
• Community Models
• weval.org & participatory evaluations

On human & agent societies

• The Habermas Machine & habermolt - where next?
• Harnessing the Collective Intelligence of AI Agents in the Wild for New Discoveries

Givers Gain is the only event I will go to that has the word "networking" in the description.

The aim of the session is to make helpful introductions for people attending the session, by combining the networks of each individual that attends. The basic principle being "I may not be able to help you, but I probably know someone who can".

Anyone can run one of these sessions. It works best with groups between 10-20 people in size. Too few and the network isn't big enough, too big and the session becomes unwieldy. Closer to 20 is probably the best size if you can get it.

The session works in 2 rounds. In the first round, people take turns describing themselves and the kind of person they'd like to be introduced to. Then there's a break, and people think if they have any introductions to make. Then in the second round, people take turns sharing the introductions that they can make.

How To Describe Yourself

You have 90 seconds in total, so you need to be succinct. The template to follow is:

• Your name (so people can make the introduction. Contact details are shared at the end)
• Your organisation or practice (the domain of life your introduction is for)
• Why you're good at what you do (why people should want to be introduced to you)

How to Describe Your Desired Introductions

You need to describe the kind of person you want to meet, the more specific the better. It might feel like being specific is ruling people out, but it actually isn't. People find it easier to create a partial match with more information than a perfect match with less information. Being specific helps people make the partial matches.

The template that I'm familiar with uses 3 levels of introductions, called :

• Bread and butter. This person is your day-to-day business, you meet and work with this kind of person all the time, but it's always good to know more of them.
• The Cream. This is the kind of person that if you met them, it would give you a great quarter, or a great year. It's your normal business, but it's a big substantial chunk.
• The Dream. This is the kind of person that could take your organisation or practice to a whole new level.

Ideally you'd have your bread and butter, cream and dream ready before your turn in the round. It takes some practice to be able to think of them and present them within the time limit.

Giving Referrals

While people are introducing themselves and describing their ideal introductions, take notes. You'll have time to think of people during the break.

The referrals should be to people, not business ideas. "Have you tried calling local shops?" is not a referral, for example.

If you give someone a referral, it's your responsibility to act on them. It can be as simple as sending an email that says "I met someone at a networking event who wanted to meet people like you, can I introduce them to you?". All you need to do is make the connection.

After the Session

After the session, keep your notes in your back-pocket, because you may come across or think of connections later and ideally this can be a regular group you attend and you make it better by creating more connections.

I would be happy to run a Givers Gain session for the cohort and faculty to demonstrate how it works.

Non-violent communication is a book written by Marshall Rosenberg. I've never read it, but from my skimming, it is similar to the communication practice taught at Community Mediation Maryland. However, there are some key philsophical differences, and I think it's aimed at different goals. I don't know for sure because I haven't read it yet! Generally it is quite popular among mediators.

Possible method of engagement: a one-time book club meeting to discuss this book, with the expectation that everyone in attendance has read it before attending. If desired, I could bring exercises to practice the skills. Noting that I have not read it before, but I do come at it with an expert eye and some familiarity with the broad topic, this would be like a self-guided skills workshop.

Exercises would be like: simulated role play, where you're given some character background and have to act out a conflict. A chance to practice the language. I don't really know for sure yet what it would look like, having not yet read the book.

I define a decision as the process of selecting one out of two or more alternatives.

In many cases, it can be valuable to put some work into generating as many ideas as possible. This is especially important for making decisions amidst disagreement — it takes some real creative work to find an idea that everyone will agree to.

In addition to finding creative ideas to reach agreement, you also simply want ideas that are good! If you are designing a technology, or planning a campaign, you don't just want to settle on the first viable idea that comes along. You want to push yourself and your group to come up with the best possible idea.

Colloquially, the process of coming up with ideas is known as "brainstorming." For design purposes, you must address two key tensions in brainstorming.

On the one hand, you want to maintain as much diversity as possible in your process — the more diverse your ideas are, the more likely you are to find a good one. If all of your ideas are very similar, you're not really exploring the possibility space. Thus, a good design practice might be to keep all of your group members isolated, so they don't start to herd together around the same set of ideas.

On the other hand, people inspire each other — we "riff" on each others ideas — once I hear an idea from you, it sparks an idea from me that I would not have thought about on my own.   So we don't actually want to keep people separate, do we? We want to keep them together, so they can bounce ideas off each other.

This is the tension you must consider in your design process — balancing diversity and creative synergy.

Another technique for generating ideas is crowdsourcing. The idea of crowdsourcing is that we can recruit members outside of a group to contribute ideas, often through a competition where the best idea gets a prize.

The advantage of crowdsourcing is that it puts a lot more people on the problem—and more people means more chance of finding a good idea. A very popular website for crowdsourcing is Kaggle.

One interesting idea I recently read (no link available) about crowdsourcing is that the type of feedback matters — sometimes, you might want to give feedback on the quality of the solutions, and sometimes you might want to give feedback on the way that people are designing the problem.   Each approach has different advantages.

These are a few examples of design principles you might consider, and we can discuss others.

With respect to developing your facilitation practice, I teach a very specific set of techniques for managing idea generation.

Causality is a branch of statistics which economists specialise in. It lets us study all kinds of interesting questions, like the impact of minimum wage on jobs, an extra year of education on income, whether a drug works, smoking bans on smoking rates, pornography on sexual assault.

Let's talk through causal analysis intuitively as well as nerding out on causality theory, e.g. the work of Judea Pearl.

See A Crash Course in Good and Bad Controls (2022), The Book of Why (2019) and Causality (2009).

I can't say much about the place before I got the keys in 2011, but I'm happy to give my account of what happened after that. When the college started, the house was practically a ruin; no heating or hot water, a chunk of the roof was missing, the front door was broken and didn't lock!

Reid Hoffman famously said "If you're not embarrassed by the first version of your product, you've launched too late", and Newspeak House was no exception. Much like it feels with AI today, it was obvious that there was something important happening with technology and politics, people needed a place to meet to talk about it, and this was the best we had.

The history that is written is a combination of the events held here (newspeak.house/events) and the people who inhabited it (newspeak.house/fellowship); maybe you can ask your favourite model to give you an overview. Otherwise, feel free to ask some questions below and I'll do my best to tell you a story!

Much of my professional work has involved working between different professions - in particular I have interfaced between officials (in governments and regulators), academics, and civil society. But also even within government I found it useful to understand differing contexts between departments and (e.g.) analysts vs. policymakers - and develop skills to understand and navigate them.

I can talk to Fellows about practical skills and approaches that can be useful in collaborating across different mindsets and professions. This will include considerations of different incentives and pressures, and methods for learning from engagements, building in particular on thoughts I began in this post:

• www.oliver-marsh.com/academic-non-academic-collaborations-also-charity-donations

This topic builds on Systems Thinking: Understanding Political Dynamics.

Simulation models give us a way to explore how politics systems behave, and what might happen when we change or interfere with them. Examples are how opinions spread through a population, how protests or political movements grow, how inequality accumulates, how institutions respond to pressure, or how different interventions might change the course of a system.

A simulation is a simplified representation of a system. We define the things in it, how they behave, and how they interact, then see what larger patterns emerge. Simulations can be mathematical, computational, physical, or even played out by people.

Traditionally, computer simulation required programming and mathematical expertise. LLMs change that. It is now possible to describe a system in ordinary language and have an LLM help turn it into a working model. That makes simulation modelling much more accessible, but it does not remove the hard part: deciding what to include, what to leave out, what assumptions you are making, and whether the results mean anything.

In this topic, I can help you will build simple models of social and political systems and experiment with them. I can help you choose a system, identify a possible mechanism, turn it into a toy model, use an LLM to implement it, and interpret the results.

The emphasis is on learning how to think with models rather than learning to code.

Core reading

• Marco A. Janssen, Introduction to Agent-Based Modeling (open textbook).
• Ezequiel A. Di Paolo, Jason Noble & Seth Bullock, “Simulation Models as Opaque Thought Experiments” (2000).
• John Bryden, Eric Silverman & Simon T. Powers, “Modelling transitions between egalitarian, dynamic leader and absolutist power structures” (2022).
• Joshua M. Epstein, “Modeling Civil Violence: An Agent-Based Computational Approach” (2002).

No, I don't have the answers of how to completely successfully go against the enormous power of Big Tech companies. But I can talk about the various types of efforts being deployed, some case studies (I list a few below, largely as examples), and where they might work together - including internationally, and by bringing in parties who do not normally focus on political technology.

• Investigative Journalism: From the Facebook Papers to 404 Media
• Political stands: From social media age bans to Palantir.
• Public pressure: From Cambridge Analytica to the Anti Data Centre Movement
• Regulation: From antitrust to tech regulation
• Internal efforts: From Project Maven to Trust & Safety
• Lawsuits: From chatbot harms to social media addiction.
• External Research: From Crowdtangle to the Coalition for Independent Technology Research

I can also talk about some of the disadvantages and discomforts that may be involved, for instance where there is division of what "success" looks like, or trade-offs between (e.g.) completely accurate and truly engaging messaging.

There are numerous opinions and camps around the ethics of various technologies (most strongly and recently, AI), which can create frictions even amongst political technologists who are aligned on other values - let alone people who do not consider themselves political technologists. I can provide advice for Fellows who wish to discuss or navigate polarised views around tech. I will not attempt to convert Fellows to any particular perspective, nor help them to do so with others. I will instead help them consider how they can locate their own work, projects, and views within the existing space; and engage with & prepare for potential criticisms and opposition.

My experience in this topic: Besides my broader work in tech accountability and safety, also at the NGO AlgorithmWatch I was responsible for a lot of work at the intersection of differering views on tech, and a spectrum of organisations who took different approaches and stances. I developed AlgorithmWatch's principles for use of Generative AI, building on surveys which showed diverging views of AI in our organisation. I was also responsible for our research strategy, much of which challenged technologies while also often using (and sometimes encouraged use of) the same technologies.

Economic models are often criticised for relying on absurd assumptions, such as perfect rationality. Despite this criticism, these models persist throughout our politics, policy, and in business.

If modelling is the art of simplifying a complex world through assumptions and abstractions, the question is not if these assumptions are true, but whether they are useful. What makes a good assumption? When does a simplification improve understanding vs worsen it? Let's discuss how economists approach these questions, from traditional modelling methods, to more recent developments in behavioural economics that adapt the assumptions with psychological research.

For further reading, Milton Friedman’s The Methodology of Positive Economics (1953)

Economic theory often seeks to identify a model's "equilibrium" - a stable state towards which the systems trends and then rests. However, many economic, political and social systems have multiple equilibria. This provokes questions: which equilibrium will emerge, what determines that outcome, and how stable is it?

Understanding this illuminates the robustness and fragility of the systems on which we rely. It also explains why seemingly arbitrary features can be highly influential.

For example...

• Why a bank can be healthy one day and collapse with a bank run next.
• How and why power within an organisation can be concentrated on a single individual.
• Why political norms can remain entrenched for decades until suddenly they are not.
• How your life could change from going great to collapsing quicker than you hoped (and, more optimistically, vice versa).

What are the different forms and ways in which the collectives and political activities you work with are imagined? Getting clear about what metaphor is in play can help us stay honest when it comes to generalizations.

Metaphor often guides processes, obscures assumptions, and impregnates practical processes.
Revealing what the imagined pictures being used in politics are can be an excellent first step in figuring out what on earth is going on out there.

Most good work dies in an inbox. People spend weeks on a project, a proposal or an idea, and then thirty seconds on the message that's supposed to get someone to engage with it. Getting things in front of people is a genuine skill and one you can learn.

I've spent years operating in ecosystems where nobody has to reply to you: MPs, funders, journalists, party staff, senior volunteers, busy organisers. I can help fellows with the practical craft of cut-through: writing emails people actually answer, WhatsApps that get a response rather than a "seen", subject lines, timing, length, asks that are easy to say yes to, and knowing when a message should be a call instead. This also includes the wider game of visibility, how to warm someone up before you ask, when to use an intermediary, how to follow up without being annoying, and how to make your work legible to people with no time.

I can help fellows think about questions like: Why is nobody replying to me? What's the actual ask here? Who should this come from? How do I get my project in front of the person who can act on it?

Political communication increasingly isn’t just about having the strongest message, it’s about understanding how information moves. I’m interested in the relationship between message, messenger, medium and distribution: why some political ideas spread while others disappear, why the same message lands differently depending on who delivers it, and how platforms shape what people see, trust and act on. Most political actors obsess over the first and neglect the other three which is usually why their ideas disappear.

Some things I've learned from testing this in practice:

• Messenger often beats message. In our crowdsourced message testing, the most persuasive messages came from people who shared demographics and political history with the audience, not from professional comms people. In our pilot work distributing broadcast clips into Reform-adjacent spaces, a named, working-class trade unionist's account outperformed an anonymous "in-group" account on every metric — roughly 5x the views — while avoiding the platform bans that killed the anonymous ones. Authenticity is a distribution strategy, not just a virtue.
• Distribution Mechanics that grab attention are different depending on what platform you are on. If you want something to grab attention on TikTok you might make a very short video with a shocking hook. If you want to grab attention in a whatsapp group locally, asking a question and telling a "gossip like" story is way more effective.
• Proximity drives salience. Ads featuring local candidates consistently beat national politicians; in one pilot, everyone who scanned a QR code on a leaflet lived within 1km of the local issue it discussed. People pay attention to politics that is recognisably theirs.

Through my work across campaigns, organising, experimentation and political technology, I’ve explored how political attention forms in practice , from local Facebook groups and messaging apps to campaign content, community networks and conversational spaces. I can help fellows think about questions like: How do people actually encounter politics today? When does messenger matter more than message? How do platform incentives shape behaviour? Why do some issues suddenly become politically salient? How do trust and legitimacy get built online? And how do you design political interventions that create participation rather than simply visibility?

I’m particularly interested in moving beyond broadcast models of politics towards understanding politics as something relational and distributed, built through networks, conversations, repeated interactions and trusted messengers rather than centralised communication alone.

A lot of political and organisational problems are not really persuasion problems, they're legitimacy problems. People often know what needs to happen but disagree on who gets to decide, whether the process was fair, or whether they trust the people leading it. I'm interested in how legitimacy gets constructed in practice: through participation, transparency, shared ownership, evidence and creating processes people recognise as fair. Following Martin Conway, I treat legitimacy as historically contingent, socially constructed and contested — the interesting question is never "is this legitimate?" but "how did it become legitimate, and why then?"

For example, the covid memorial was not legitimate but it was made so. This was done by making sure the first volunteers wore flouresent jackets, had a sign put up and by the afternoon multiple front bench politicians had visited it. Then the council accepted it and paid for its upkeep as a historic memorial. Its legitimacy was constructed. Technically everyone could have been arrested for criminal damage.

Another example in the #Honourtheoffer campaign which was crearting legitimacy through organising information. When Oxford alumni felt colleges weren't honouring their access commitments, the concern was fragmented across dozens of colleges with decentralised decision-making, which meant if one fell, they all would. A hashtag gave people shared language and over 8,000 signed the petition, but the pivotal move was mundane: a public spreadsheet listing every college against its commitments, updated openly and pitting them against each other. The spreadsheet didn't just record reality, it organised it and whoever structures the information becomes trusted to interpret it. League tables, polling and Wikipedia all work this way.

Across my work I’ve repeatedly operated in environments where authority is fragmented such as campaigns, parties, community groups, MPs’ offices, networks and coalitions. I can help fellows think about questions like: How do you make decisions people will actually follow? How do you create buy-in without formal power? How do you design participation that is meaningful rather than performative? How do you create legitimacy for new technologies or organisational change?

• Martin Conway https://www.history.ox.ac.uk/publication/213503/manual

A lot of people understand UK politics formally but not operationally. I can help fellows understand where decisions actually get made: the relationship between parties, MPs, special advisers, local government, campaigns, funders, members, parliamentary structures and informal networks.

My background includes working in parliament, running Labour Together, convening election review processes and spending years operating between campaigns, think tanks and politicians. That means I can help translate abstract questions (“How do you influence Labour?” “Who matters?” “What does Parliament actually do?”) into practical strategy. I’ve run sessions on navigating UK political systems, identifying pressure points and understanding the difference between formal constitutional power and real-world political leverage.

Politics rarely works the way the diagram says it does. A lot of strategy is understanding timing, relationships, legitimacy, incentives and institutional rhythms.

One of the things I spend a lot of time helping people with is moving from “this is unfair” to “this is changeable”. Political systems often look impossible until you identify where decisions, incentives and coordination failures actually sit.

I can help fellows map systems and identify leverage: who controls implementation, who shapes narratives, where informal veto points exist, where legitimacy matters, and where small interventions create outsized effects. This includes practical tools for actor mapping, coalition design, identifying hidden decision-makers, sequencing campaigns and understanding institutional incentives. I’ve run sessions that focus less on ideology and more on operational politics: how things move in practice.

I can help fellows think through different routes to change: insider versus outsider strategies, coalition building, narrative pressure, institutional entrepreneurship, campaigning around moments of opportunity and understanding how influence accumulates. This includes practical questions like: Who needs persuading? What kind of evidence matters? When should you campaign publicly versus privately? How do you build influence without formal authority?

I’m particularly interested in helping people understand that lobbying is rarely just meetings, it’s often about creating conditions where a decision becomes easier to take.

Ignorance of native american political ideas is a generalized feature of western and eastern political thinking. The Arhuaco Tribe of Colombia have resisted colonization, both physical and intellectual, until the present day - a story which counts as a miracle of resilience.

https://www.youtube.com/watch?v=j3mMB_BSTk4&t=32s

The tribe has institutions and traditions of deliberation, dialogue, and decision-making which are independent of enlightenment western norms, making it a fascinating and rare glimpse of diversity in decision making collectives.

Over the past 10 years I have had the privilege of researching, interviewing and living with Arhuaco leaders and the wider community.

A lot of civic and political technology fails because it solves an imagined problem rather than a real one. Through Campaign Lab and product experimentation I’ve spent years thinking about how political tools get adopted and why they usually don’t.

I can help fellows move from idea → user → workflow → adoption → scale. That includes discovery, prototyping, testing in live environments and designing for exhausted campaigners rather than ideal users. All too often technologists want to create perfect solutions and go through extended processes involving lots of user time and energy. When this approach meets politics it often fails.

I’m particularly interested in questions like: Why do organisers abandon tools? How do products fit into existing workflows? When should you build versus adapt? How do you know if a product is creating value? When should you work with partners to embed something?