Estimation and prioritization for a product roadmap

We started our Product Management course digging into a new feature release of a School Admin product. School Admin works with independent schools to provide software that they can use to automate all kinds of school administration tasks. The release that we are working on is a parent-facing portal that facilitates online applications to client schools.

We first documented the features and then facilitated a discussion with a software engineer to get an estimate for how long the project might take. The first engineer I talked to was my friend Sunny. Facilitating that conversation, I let him organize the features into categories that were meaningful to his work as a software engineer (navigation, data management, styling, sub-pages). He then gave me estimates for each of those categories.

Afterward, as I was thinking about prioritizing features for a thin slice version of the app, I realized Sunny’s estimate wasn’t really going to help me. I reached out to another software engineer, Matt, and walked him through the features and asked him to provide an estimate while sticking with my categorization around features. The estimate ended up actually being substantially shorter (a little more than 5 weeks, compared to about 8 weeks), which I attribute to natural variation. Both engineers insist they were giving very conservative estimates.

With Matt’s estimate, I was able to think about what features seemed worth investing time in. Right away two places to scale back came to mind. First was a component to add an applying student to the portal that included a sub-component that asked to identify who this student was a sibling of. The flow of this feature seemed potentially confusing. Is step-sibling a sibling? If one parent did the application for one child last year and another parent completes the process this year, would the portal prompt this different user?

As I explained how it might work, Matt indicated that it might complicate existing data structures. The estimate for the ‘add student’ component was substantially increased by including the ‘add sibling’ subcomponent. For something that to me had questionable value, it was easy to de-prioritize that.

The other possibility I considered for scaling back was the student dashboard and the application checklist. Both of these features included a visualization of the student’s progress as an applicant. Although seeing the application progress in both screens was nice to have, they were performing the same function for the user in different ways. Twice as much work for the developers, but not twice the value for the user.

Although I had an intuition about these two features being the place to trim fat, I wanted to approach the question more systematically. I used a 2×2 to graph the value to the user against the effort required to deliver the feature or component.

prioritization chart

I then created a product roadmap for the full product. I put features that served as containers for other features first (such as the menu and sidebar). I also prioritized the fields that can be customized by the schools as they might end up needing more robust testing because the school users might get really weird is chosing content for those areas.

Parent Portal - Full Product

Visualizing the process this way I realized two things. One, the notification center icon was a substantial chunk of the roadmap. I hadn’t even visualized it on my 2×2 thinking of it as trivial. Two, while my Matt and I had discussed the complexity of the ‘add sibling’ sub-component, I hadn’t actually asked him to break down the estimate into one estimate for the component without the sibling option and with it. If Matt and I were actually working together on a team, I might have sent him a quick slack message for clarification, but because he was just helping me out with this project, I decided to work with the info I had.

My ‘thin slice,’ a scaled-back version of the product with 80% fewer resources in terms of developer time, emerged with ‘add sibling’ component intact, but without the dashboard status display and without the dynamic notification icon. The link to access the notifications is still there, but does not display differently in response to new notifications. The redundant status display on the student dashboard is removed. Now a parent will have to click on the application checklist to see their student’s progress toward completion.

Parent Portal - Thin Slice

With testing and feedback from users on both the school administration side and the parent applicant side, I might have made different choices. Will it annoy schools to send notifications that parents never see because a red blinking message icon isn’t demanding their attention? Will the lack of a progress bar at the top level screen lead to lower rates of application completion? Perhaps.

While talking to Matt and Sunny they both emphasized that the time estimate for a project doesn’t reduce by half when doubling the number of engineers on the project. Context switching and time spent coordinating work and making sure endpoints match up reduce the efficiency of projects with mutliple developers working on them. To minimize inefficiencies my roadmap suggests an order, but defers to the engineering team to allocate work within the team.

Prototyping Vouch

This is the sixth installment of our team’s (Allison, Laura, Michelle) project for our Studio and Ideation class. This project builds on the research we did with gig economy workers last Fall, which you can read about here and here.

After feedback from last week, we decided to dedicate ourselves fully to Vouch, a service that makes it simple for friends and family to lend money and build trust. In Vouch’s current state, it’s primarily used as a simple method to get both lenders and borrowers on the same page. We heard in our research that loans between friends and family are often messy when terms are not decided upfront. 

With Vouch, both borrowers and lenders can quickly fill out a form to determine the loan amount, interest rate, payback schedule, and any other options like equity or collateral. Vouch serves as a point of truth. One of our main design principles is for Vouch to inform and foster connection, but not mediate. 

Our primary goal for this week is to bring the idea to life as an MVP and validate whether this concept has legs to take us into Q4. 


Our progress this week

Built out our website including a landing page and two pages.

  • We revisited our value props and focused on transparent terms, simple reminders, and shared loan tracking. You can see the entire website here:


Created forms to serve as our customer intake. 

    • Created a diagram of the user types that our product can serve to think through on-boarding flows.Screen Shot 2020-02-21 at 7.30.49 PM
    • Using Typeform, we created conversational forms that allow both borrowers and lenders to easily disclose the terms of the loan. This is our on-boarding mechanism for Vouch users.
    • See for yourself: Form for Borrowers, Form for Lenders
    • We hypothesize that borrowers will be our primary audience, but we wanted to build out both flows and test to validate that assumption. 


Drafted a System Diagram to show user touchpoints. 

      • Our system diagram quickly shows how little the user has to do. One of our goals was to keep things clear and simple for users. 


Built out our communications flow, copy and visual assets.

    • Terms editing, reminders, and payment confirmation all happen through text or email, so we drafted responses for our eight possible flows. 
    • Read our copy
    • We also created visualizations of the repayment process that can be included in communication with borrowers and lenders.
    • vouch-data1 vouch-data2


Posted to Craigslist and other forums to find customers.


Reached out to friends and family to recruit customers.  

Insights & Feedback

We did a few talk aloud exercises as users walked through the lender forms for new and existing loans. The purpose of the talk aloud was to capture immediate, instinctive feedback. We asked questions about what felt good and clear, what gave pause or hesitation, whether they saw value in the service, and whether they would recommend the service to someone else. 


What do you like about the service? 

  • “I like the idea that you take the anguish part of it out of the lender. Vouch would be cushioning the blow.” 
  • “What I really, really like about this app is that you’ll do the reminding. Because I’ll either be ignored or hear excuses.”
  • “It shows me I got to be stricter about my expectations. Because I don’t set up any expectations, I don’t set up any plans.”
  • “Neat, clean, clear, simple. I like that it’s not a whole lot of information. Or too glitzy or whatever.”
  • “Getting this to college students who I would think, off and on, would be borrowing money – ‘Okay Mom and Dad I found this thing called Vouch – let’s do it this way.’ Working from the opposite end to attract boomers to it. Having the borrower approach shows initiative, responsibility.”


Would you recommend this service to anyone else? 

  • “My friends – we don’t talk about money that we loan to our kids. Usually it’s a very private thing between people… It would be so rare that I would tell anybody about it.” 
  • “I think it lays it out clearly – the things that could be discussed – to protect everybody and to keep on good terms so that both of you are on the same page with the borrowing and the lending. Not everyone wants to borrow money but this must make it more comfortable for them knowing there are standards and expectations that are clear.”


Notes on language

  • “Being on the same page is a manner of speech that we understand, but is a colloquialism that is culturally specific.”
  • What’s the name of the person – kind of casual. Maybe that’s on purpose to be friendly but in all my professional writing I’ve never used contraction. I thought oh, maybe this isn’t so professional. That flashed in my mind.”


We received feedback that the terms could include more room to articulate the flexibility and understanding that often exists with this type of loan. 

  • “If you miss a payment, how would that work? How would it know if you didn’t pay me for a couple months or I knew you were in dire straits?” 
  • “I can’t just look at things as cut and dry. I look at situations.”
  • “Repayment to family gets put behind things that are more important – it’s easy to get pushed aside because we’d be understanding.”


There was a suggestion for a pause and resume option, affording the borrower time to get back on track. Flexibility and understanding is partly why we borrow among friends and family. Due to the intimate nature of these loans, folks are often well aware of the situation or struggle a borrower may be in – highlighting that help extends well beyond the cash assistance. How can we adequately represent this moving forward? 

Next Steps

For our last week of Q3, we want to edit our sales deck to be a really tight, succinct pitch for Vouch. We’d also like to gain real users (not just friends and family) and hopefully — get a few success stories. Lastly, because our goal is to get borrowers and lenders on the same page, we want to design a terms sheet that is simple, straightforward, and easy to understand. User feedback will help us iterate on how best to visually represent the terms so we’re eager to get this in play. 

Ideating with Insights from the Gig Economy

This is a progress report on our team’s (Allison, Laura, Michelle) work with the gig-economy. For a quick look at our concept maps from last week, go check out last week’s blog post. For a better understanding of our research and the focus of this project, see posts here and here

Progress Made This Week

Identified 79 design patterns that we used to help spark inspiration when coming up with design ideas. These patterns ranged from topical trends (deep fakes and ASMR videos) to grander shifts in design and culture (pressure on organizations to take a stance on social and political issues).

Sorted 100 insights into 13 insight categories which allowed us to understand higher-level trends in our data. Our categories with the most insights are “Gig Beliefs”, “Narratives Society Tells Us” and “Access”. To help better illustrate these categories, here are top insights from each:

  • Gig Beliefs: Gig work is glamorized as a choice of freedom and a path toward autonomy. 
  • Narratives Society Tells Us: The American Dream is hostile to anything other than the pursuit of economic success. 
  • Access: Gig work creates access to the workforce for those who have historically been excluded.


Developed 201 design ideas by randomly mashing up design patterns and insights and using that as inspiration. Because our design patterns went beyond “Uber” or “AirBnB” and instead focused more on behavior, we were able to come up with some very out-of-the-box ideas that were not solely rooted in popular app trends of today. A few fun nascent ideas include:

  • Flat Stanley for executives to take to their office and show students who may not have access to that world what their days are really like. 
  • Access to exclusive events and opportunities that can only be unlocked by contractors with a gig company. (Think of a stage at SXSW sponsored by Lyft where all of the performers have completed at least 30 rides as a Lyft driver.)
  • A storefront where the items are priced relative to the buyer’s hourly wage, as a commentary on inequality in society and the different meaning that status symbols have to different people.

Visit here for a complete look at our design ideas, patterns, and insights. 


What We Learned

  • We started using a reframing process to generate ideas. This ended up being successful at generating new, useful insights, but not design ideas. Because our focus this week was on ideas, we deprioritized our work with the reframing model, but look forward to revisiting it next week or beyond.
  • While design ideas pertinent to the gig economy are still interesting to us, we generated many ideas related to the future of work generally or even more broadly, related to dominant political, social and economic paradigms.
  • Although many of our ideas were focused around technology and apps, we challenged ourselves to veer away from these comfortable tropes to explore physical products and spaces as well as experiences and events as those often yielded ideas that were more unique.
  • One of the challenges of this program is to become less precious about what we put out into the world. We do this by writing a million blog posts, generating a million post-it note ideas, delivering countless presentations and drawing innumerable figures. Ultimately, we begin to think less preciously of ourselves in the process. 
  • Participant data is still coming up in the ideation and insight process. At times it seemed we were moving further away from the research and further away from the actual people but as we wrote ideas and thought through what might be useful or meaningful or funny or bad, we recalled things our participants said and experiences they had shared with us or behavior we had observed. It feels like we are truly designing with our participants because their language, impressions, and insights remain at our fingertips. 
  • We tried a technique of mashing up a randomly generated word with insights to arrive at a new idea, which was not particularly fruitful for us.

Next Steps

  • Next week we will focus on sorting our ideas and filtering for ones that most resonate for us as a team and with the data that we found most compelling in our research phase.
  • We’d like to facilitate a round of read-throughs with the other design groups to rank up ideas that are especially resonant and add to each other’s walls. 


A future mindset for design ethics

“Our greatest responsibility is to be good ancestors.” – Jonas Salk, inventor of the polio vaccine

I consider myself a futurist. As an educator, I couldn’t help but become one when I began to understand the way that my work in the present shaped tomorrow as my students continued to change the world in big and small ways. Each small step–teaching someone to tie a knot, find the standard deviation of a set, or how to debug code, was an investment in our shared future. Knowledge I shared with a student might be practiced immediately, but it also built a foundation for a life of exploration, curiosity, kindness and confidence. Although I never taught a course called “How to Change the World,” I realized I was doing exactly that.

James, an outdoor education student who savored every opportunity to cook group meals with me over our tiny backpacking stoves, now owns a restaurant and butcher shop in Oklahoma City. Our course was his first chance to take care of other people by preparing delicious meals at the end of a long day. Alex, a software engineering student who struggled with concepts that her peers grasped easily, is now an engineer at Apple. I coached her extensively on cultivating a growth mindset and tackling impostor syndrome. Those tools must have been as important as her engineering skills when interviewing for her current role.

The futurist mindset that inspired my work as an educator has developed further as a design student. As I conceive of the work we are doing as students and imagine future design work creating the world that I want to live, I am both excited and a little afraid. Every single beautiful or awful aspect of our society that exists today is the byproduct of choices made by individuals. As such, I am mindful to wield my influence with not only a sense of responsibility, but also empathy and compassion.

While some of the biggest challenges of our time may seem beyond our ability to solve, I know that we are creating the future every minute. When developing an ethical framework to support my work as a designer, I wanted to balance a sense of caution with optimism. My framework is impact-oriented, but also acknowledges our inability to perfectly estimate the outcomes of our work. In building my framework I attempted to include aspects that acknowledge our place in history and the potential future impacts of our work. Ultimately, I decided that a futurist mindset was best addressed not by having specific elements of the framework speaking to timescale, but by embedding ethical review as a practice that needs to be repeated at intervals in order to combat the limits of our ability to see into the future.

I have tried out my framework at multiple inflection points of a single company. In the past 15 years they’ve been known by several names, Ploom, Pax or Juul, but the two founders have remained throughout. They were two product design students who met at the Stanford, learning many of the same things that I am learning now. And all that thoughtful and empathetic design practice and prototyping led them to design a product that has reversed decades of trends in nicotine addiction amongst teenagers. I wanted to explore their story through an ethical framework to better understand how well-meaning, intelligent designers could end up creating such a destructive product.

My primary conclusion is that the two founders, James Monsees and Adam Bowen, were like the proverbial boiled frog who slowly perished as the water got warmer cooking him without ever realizing his peril. The product vision at the outset, a harm reduction product for current smokers was benign, but not riskless. What eventually resulted was a product optimized for addiction and unleashed with sexy marketing targeted at young people that was ultimately acquired by the largest tobacco company in the world. I imagine that at the outset, James and Adam would not have predicted this outcome. When I consider what went wrong, I can’t overlook the limits of the two founders to anticipate the outcome of their choices in 2005 when they were students starting this company as a student project.

Screen Shot 2019-12-12 at 6.01.41 PM Screen Shot 2019-12-12 at 6.02.06 PM

While our ability to anticipate outcomes declines as we peer further into the future, the consequences of our actions can grow ever greater.

Screen Shot 2019-12-12 at 6.01.18 PM

Does that mean that those destined to be good ancestors are people capable of great foresight? Or are they just lucky that their high-impact decisions ended up having positive outcomes?

Although both of those are possibilities, the way that we best position ourselves to be good ancestors is by course-correcting throughout our journeys, to stop, reflect, reevaluate and change course when needed. Our imperfect ability to see the future can be augmented by planning times of reflection into our project timelines and into our personal and professional lives.

How Leave No Trace ethics can make us better designers

The ethical framework I use most frequently is Leave No Trace. It is an impact-oriented set of rules to mitigate human impacts on nature when camping or exploring the outdoors.

The six principles are:
-Plan ahead and prepare
-Camp and Travel on durable surfaces
-Dispose of wastes properly
-Leave what you find
-Use fire responsibly
-Respect wildlife


When I think about my natural orientation towards ethical questions, I can’t help but be influenced by my values and practices in relation to land use and sustainability, which is also largely impact-oriented.

A focus on impact is an ethical framework is in contrast to other lenses such as a focus on duty or virtue.

Duty: “It is my duty to not litter.”
Virtue: “It is morally wrong to take pine cones from the forest because they do not belong to me.”
Impact: “I must be careful to put out my campfire because a forest fire would be devastating to the plants and animals here.”

Impact seems like an important consideration in living an ethical life. A world of people all creating positive impacts feels like a world I want to live in, even more than a world of dutiful or virtuous people. However, impact is also the least ‘knowable’ aspect of an ethical decision. It is necessarily ‘post hoc.’ We won’t know how something is going to play out until it does.

How can we maximize positive impact given our naivete about the consequences of our actions?


Control for bias
We are bad at estimating negative impacts, we have a bias towards assuming good outcomes because we know our good intentions. We tend to overestimate the likelihood and magnitude of positive outcomes and underestimate the likelihood and magnitude of negative outcomes.

As designers, we can recalibrate our risk tolerance and our bias by being aware of these tendencies and making and effort to add in the possibility of negative outcomes to our planning. We can also practice the precautionary principle, by assuming negative outcomes until we have evidence to the contrary.

Do your homework
To better estimate the possible impacts of our work we have to do our homework. Sometimes this is in the form of prototyping and testing iterations of our designs with populations before releasing them at scale.

Sometimes it means investing in educating yourself about the struggles of marginalized people, fragile environments, unequal systems, and vulnerable stakeholders so you can better estimate the consequences of your design choices. Impact-oriented designers create a space for people with outsider knowledge or positioning.

Find your center
When thinking about our impacts we can be more intentional if we are deliberate about who we are centering when we are evaluating impact. In LNT the wilderness ecosystem is at the center. When striving to make positive change it’s easy to say that we are ‘human-centered’ designers, but what does that really mean in practice? And what other interests are at risk of stealing our focus?

It’s not enough to just center the entirety of human experience. Being more precise about who our technology is for and how it will help them allows us to more accurately estimate impact. We can’t be all things to all people when trying to orient design work to impact.


Through controlling for bias, doing our homework and finding a center we can apply lessons from decades of Leave No Trace ethics to design work.

Understanding blockchain starting at zeros and ones

When I was at UCLA I took a GIS class taught by Tom Gillespie that changed my life. I actually took many classes that he taught (maybe four). The first convinced me to add Geography as a double major and the GIS class was my first time really understanding an important flaw with the things we are asked to do in school and left me with a mindset about how to do school and later how to do work that has stuck with me ever since.

In this class we were learning how to use arcGIS and publicly available data sets to solve problems. This quarter-long class culminated in a final project that was worth about 80% of our final grade. There were no smaller deliverables, no formal check-ins along the way, just one massive project. And there was very little guidance. A bare-bones project brief, no rubric, and whenever we asked him to clarify ‘Should it do this?’ ‘Should it be this?’ ‘Should it look like this?’ He would answer cheerfully in his booming voice, “Do Good Work!” 

He told us to rely on our inner north star to tell us if we were doing good work. Is it something you’d like to see in the world? Does it make the world a better place? Does it advance knowledge? Learning to listen to this voice would be more important than learning to conform to a list of requirements on a rubric. People who change the world in big or small ways are not using a rubric. At the end of that class, we all ended up having lots of good work to show off, after working through the anxiety provoked by such an ambiguous assignment. 

In the early weeks of the class we logged dozens of hours of the GIS lab trying to make something that would impress our professor, but by the final weeks of the class we were logging time there working on projects we were passionate about. So in the spirit on deviating from the rubric, and with Tom’s voice in my head, I’m departing from the assignment briefing  in an expression of my own ethics around ‘doing good work.’ 

This blog post somewhat touches on ethics, but primarily is an explainer about blockchain, the one that I wished I had stumbled upon when I didn’t know anything about how it all worked, because it felt like it could be an asset to the conversations we were having in class and I hope that anyone who reads it might feel like it’s good work.


A lot of understanding blockchain rests on understanding the fact that all of this is built on good, old-fashioned math done with good, old-fashioned numbers. Between discussion of bits (data encoded in binary as 0’s or 1’s) and long strings of numbers and letters, it’s hard to visualize how there could be actual math involved. The mechanics of blockchain are happening with real numbers that can be added or multiplied just like the math that we did in school. 

Technical writing about blockchain will often drift back and forth between discussion of different types of numbers without being explicit about their relationships to each other.

Four ways of representing a number that come up a lot in blockchain talk:
Binary (zeroes and ones)
Regular numbers (all the numbers you learned in Kindergarten, 0-9)
Hexadecimal (numbers plus letters A, B, C, D, E and F)
Base58 (numbers, plus uppercase letter, plus lowercase letters, minus lookalikes)

If I want to express a number in binary it takes a lot of digits because each digit can only express two options, zero or one. If I limit myself to three digits of binary I can only express 8 numbers. (This is 2x2x2 or 2^3, because for each digit there are only two possibilities.) For a number as big as 100 I need 7 digits. For a number as big as a million, I would need 20 binary digits. 

Regular Number Binary
0 0
1 1
2 10
3 11
4 100
5 101
6 110
7 111


If you hear someone talking about ‘bits,’ they are talking about binary numbers. Computers store data as 0’s and 1’s, which we all know (because we’ve seen “The Matrix”), but binary numbers are actually really hard for our human brains to understand. And they take up a lot of space when we are representing them visually, like on a screen. When we want to deal with computer numbers we can translate them to regular numbers to make them shorter or hexadecimal to make them even shorter. 

Each digit of a regular number can be one of ten different values (0, 1, 2, 3, 4, 5, 6, 7, 8, 9) which means to express a number as big as 100 I need (wait for it…) 3 digits! There’s probably nothing about how regular numbers work that I could tell you that you don’t already know. With hexadecimal, each digit can be one of 16 different values, so I only need two digits to express a number as big as 100. It’s a marginal improvement. 
Base58 is a way to more substantially compress binary data into a shorter form. It takes all the lowercase letters (26) and add the upper case letters (+26) and adds the numbers (+10) and then subtracts 0 (zero), O (capital O), l (lowercase L) and I (uppercase I) to give you 58 characters to work with for each digit, making the compression much more effective. Base58 is a variation of an earlier version, Base64 which keeps all the letters and numbers and adds + and /. The reason for removing those was to make it easier for humans to unambiguously read and write these long strings of numbers. (Human-centered design in blockchain?)

Here’s my childhood phone number in binary, regular number, hexadecimal and Base58.

Binary Regular Hexadecimal Base58
100110000010101001000001111010011 5105812435 1305483d3 8nBd9t
33 digits 10 digits 9 digits 6 digits

So the answer to the question, “What does the blockchain ledger actually look like?” has three answers. One is that it is just a series of 0s and 1s, which is how your computer is storing them. But then often users are converting them into hex or Base58 and when people are displaying them online, writing them down or talking about those numbers. But at the heart of blockchain is a lot of math, all of which is done with good, old-fashioned regular numbers. And if people wanted to they could talk about it in regular numbers, but the convention is to use hexadecimal or Base58. And if people really wanted to they could do math in Base58, but that sounds terrible.

The files themselves then are long strings of numbers that encode information corresponding to transactions on the blockchain. When people look at them they can use a parser, which takes information from the chain and makes it more palatable to read or use. I really like this parser because it retains much of the original data, but parsers like this one that give the data more structure and formatting are much more commonplace.



To send and receive transactions with Bitcoin, you need a private key, a number that identifies you and that only you know. Unlike your credit card number or your driver’s license number, you get to choose it! It can be any number below 115792089237316195423570985008687907852837564279074904382605163141518161494337. That’s not even a joke. It corresponds to 256 bits. So it would be a binary number that was 256 digits long. Most people use some kind of random number generator to make theirs. Most people convert their private key to hex or Base58 for ease of writing/remembering.

Using math developed by cryptographers in the 1970s you can turn your private key into a public key. Any private key will only transform into one public key and the operation cannot be reversed. Most math operations that are familiar to us are reversible. I can divide 56 by 8 to get 7 and then I can multiply 7 by 8 to get 56. However, this math only goes one way. You cannot use the public key to derive the private key.

This one-way math appears again when creating a signature for a transaction in the ledger. You use your private key, a random number and the transaction data (your public key, the recipients public key, the transaction amount) to do math that results in a signature. When comparing this signature against your public key with more math, you can verify that the signature could only have been created by someone who knew the private key, however this math does not reveal the private key.



It is an ever-growing set of files that each record transactions or other data. Many people keep copies of the files and update them regularly. You can add to the blockchain, but for the most part, you cannot change or remove things in the past. Additions are made one block at a time. Currently, the most actively used and discussed blockchains are for cryptocurrencies. Cryptocurrencies are made-up currencies that aren’t associated with any government but can be traded by users of the currencies and even exchanged for government-backed currency. 

The blockchain stores the history of the transactions as a means of verifying that the transaction took place. The oldest and most-discussed cryptocurrency is Bitcoin, but there are tons of other cryptocurrencies and blockchains that are used for other purposes such as identity verification. For this article if I use an example of a blockchain it will be Bitcoin, however, those terms aren’t interchangeable. Bitcoin is built on a blockchain, but not all blockchains function just like Bitcoin. Understanding Bitcoin is a good place to start because it was the first widely-used blockchain and most newer blockchains function in almost the same way as Bitcoin.

Lots of places! Tens of thousands of people have a copy of the Bitcoin ledger on their computers. If you remember the days of Napster and Limewire, you are familiar with the idea of peer to peer sharing. Instead of having a file hosted on a single server, many people hold copies of it on their personal computers. This is one of the things that keeps it secure. Even if you change one copy of the ledger, the other ones have the original data, so other people know yours isn’t the true one.

Blockchain files are often a lot bigger than your Smashmouth All-Star mp3, so it’s a pretty big commitment to keep a copy of it and keep it updated as new additions to the ledger are being made all the time. The current size of the Bitcoin blockchain is about 160gb. Why do people want to do this? Hard to say. The excitement of being part of a movement? Civic responsibility? Keeping the blockchain files on your computer doesn’t involve any pay out.

Anyone who has a Bitcoin transaction to record adds that transaction to the ‘memory pool’ (a term I find disturbingly Orwelian). This is just a list of transactions that people want to have added to the blockchain. Each transaction identifies the giver, the recipient, and the amount. It is signed with a signature (which is just another number) through complicated math can be used to verify the authenticity of the person giving the money.

While storing the blockchain is unrewarded, building blocks is very lucrative. ‘Mining’ is the term used to describe the work done by computers mathematically vetting transactions in a queue waiting to be added to the blockchain. And it’s not just one miner working on one block, miners compete to add a block to the chain. For anyone single block there are thousands in competition to add the next block. 

Many of transactions in the memory pool promise a small percentage as a payout to the miner who does the work of bundling a bunch of transactions into a block. In addition, there is a reward to the successful miner which can be worth tens or hundreds of thousands of dollars (depending on the price of Bitcoin). A pretty amazing reward for ten minutes of work, however, most of that huge award goes back into paying for the electricity that powers the process.

The math involved in vetting the transactions is highly energy intensive because it is so complex. Once the bundle has been vetted, there is one more step to getting is accepted as a block, which is a process that involves multiplying huge random numbers together to try to hit a target that was defined when Bitcoin was first built. It adapts to how many miners are competing to keep the rate of blocks being added to approximately one every 10 minutes. 

The high-intensity energy consumption is intentionally built into the system–a feature, not a bug–to slow down the process to give all the computers storing copies of the blockchain time to sync up before the next block is released. It is also a way to ensure that the blocks are accurate. If you put a ton of energy into adding a block, only to find out that it contains errors or inconsistencies you won’t receive the payout for the block. People are incentivized to add a ‘good’ block, not just any block.

Whose auditing these blocks? Other miners, who want that payout to go to them, rather than the person who added the latest block. At any given time there are a few different versions of the blockchain propagating through the network of people who have the blockchain stored on their computers. The most recent blocks are considered ‘candidate blocks’ and there might be a few different contenders for that same block in the chain. 

Miners choose which blocks to build their next block on top of based on which candidate they think is going to be the block that propagates to the majority of the block-keepers. If they chose wrong, their candidate block built on top of an unsuccessful candidate cannot be added to the blockchain. Miners will choose blocks that have been assembled by ‘trusted’ miners before building another block on top of theirs, or will check candidate blocks for errors.

While mining was once somewhat accessible to casual cryptocurrency enthusiasts, the arms race for more powerful machines to outcompete other miners has led to the community of miners becoming quite small and not really a community at all. At the outset Bitcoin ran on the enthusiasm of a fandom galvanized around the idea of fortifying currency against unstable and untrustworthy central banks. Today the Bitcoin machine is powered by financial interests, primarily in China who are motivated to develop the most efficient ways of extracting the value from mining.  



Is blockchain technology really a sustainable strategy for commerce at a global scale? Bitcoin is still pretty fringe, and the energy involved in adding to it is astronomical. Could cryptocurrencies scale globally to replace government-backed currencies? Probably not. Blockchain in its current form is not really scalable, especially for one currency to take on a dominant role. One might argue that was always the point. Lots of blockchains are more resilient that one single blockchain. However, even an ecosystem of smaller blockchains would have problems related to the amount of work needed to be done per transaction. It’s hard to imagine even multiple cryptocurrencies having the capability to record as many transactions as credit card companies do every day.

Another big issue is the environmental sustainability of blockchain. As mentioned above, the energy consumption involved in Bitcoin is not negligible. Due to the complexities of the calculations that are performed in the growth and maintenance of the blockchain, a single bitcoin transaction can use as much energy as an entire household would in a month. Even as a technology that has not yet been widely adopted the carbon footprint of blockchain is devastatingly huge.

The idea of using blockchain to decentralize economic power has unintentionally led to a concentration of power in an unexpected place: small, well-financed groups in a communist country who aren’t really accountable to anyone. Many people are ceasing to find that more reassuring that trusting central bankers who are at least somewhat democratically accountable in most countries.

Along with the energy costs of mining, this recentralizing of power is one of the biggest flaws in the way Bitcoin was designed to use blockchain technology for financial transactions. Other applications of blockchain technology have arisen that are attempting to build a better blockchain. In particular, the ways of verifying a block and rewarding that verification, called “proof of work,” has been reimagined to be less energy-intensive and less likely to result in centralizing the work to people who have the means to engage in it on a large scale. “Proof of stake” is one alternative that is being explored to address these issues.

Congrats to all who made it this far! If you are still looking for more info, check out these links below. And please let me know if you have any suggestions on how to make this blog post more accessible or clearer. I hope to be reworking it in the future!

Resources I used to write this post: – a great resource for going a little bit deeper into the technical aspects beyond the scope of the post. – more about private keys, public keys and signatures – the best bitcoin themed doodles on Twitter

My interview with my friend Sunny (there is one swear word, so sorry) – – an excellent RadioLab podcast about people starting a new blockchain – a great New Yorker article also available as audio that talks about the crypto landscape – a quick explainer from Wired 

Ethical Dilemmas of a Hypothetical Healthcare startup

During our class on Ethics in Design, I had the opportunity to facilitate a discussion on the ethics of privacy. We had all read a collection of articles on current issues in the privacy landscape relevant to designers, and using those as a jumping-off point I wanted to create a space for us to synthesize those ideas and continue to hone our individual points of view about the implications of privacy laws and industry practices. My goals were to create a space for people to process the readings and develop new ideas together. 

The Warm-Up

We started with a personal privacy inventory. I shared some brief scenarios of personal information sharing and asked people to reflect on how they felt about sharing this data and then summarize that feeling with a word or an emoji. Responses ranged from  ‾\_(ツ)_/‾  to “HELL NO!!” I wanted to start our conversation by placing ourselves in the context of a user who has their own opinions, boundaries, and concerns about how corporations, governments or strangers might interact with our personal data. We discussed the range of responses and considered common themes that emerged within and between participants. Do I consistently trust the government more with my data? Or do I trust corporations more? What about having my data publicly available? Remember phone books? Why did having your address and phone number available there feel less ‘creepy’ than having it public on your facebook page might feel?

The Main Event

Next, I wanted us to shift our mindset from being a user to functioning as a designer. Borrowing from the tradition of the ‘murder mystery dinner party,’ I created a scenario for us to consider synthesized from a couple of real-world examples of privacy dilemmas faced by companies that require access to personal data to provide their services or products. In particular, I was intrigued by uncovering that the HIPAA rules are only enforced on covered entities–namely, health care providers, insurers, and research institutions. This creates an interesting ethical ‘gray area’ in which policymakers have clearly established best practices for managing patient health information, but for the entities outside the narrow purview of the legislation, there is no enforcement. This loophole means that the many apps and third-party companies that have popped up in the healthcare space are not accountable to any rules about their handling of patient health information.

I imagined an app that attempts to reduce unnecessary costs borne by families who unintentionally sought health care at facilities that were not well-matched to their concern (i.e. visiting an emergency room rather than a primary care physician, or an out of network clinic rather than an in-network one). The app was serving as a link between people seeking health care, their insurer and providers while moving patient health information between all three stakeholders. I shared a hypothetical problem that this startup was facing in regards to how they were managing patient data that they were using to provide this service. This kicked off a guided a discussion that allowed us to grapple with the options available to our company and the implications of those choices. 

We asked and answered many questions. Do we have an ethical responsibility to comply with HIPAA even though we aren’t legally compelled to comply? Are our users operating with a false assumption about our compliance with HIPAA standards? Is there a risk of being”outed” as non-compliant? What might the consequence of that be? Are we exposing our partners (insurers and providers) to liability by not being fully compliant? Are there ways of mitigating the risks of associated with transferring this data between parties? What if there was a third-party company with an API that we could use to outsource some of that risk? Would it be ethical to transfer responsibility and accountability if it meant also ceding control? 

Taking it up a notch

Then recognizing that designers work at the intersection of many different stakeholders within their organizations, I challenged my classmates to shift their perspective from that of a designer to the perspective of another person within this hypothetical company. I gave each participant a card with a role printed on it (Investor, CEO, VP of Marketing, CTO, etc.) and some more information about considerations that were specific to each of those roles. We continued the discussion with this new framing and attempted to answer the question, “What should we do next?” This generated ideas not just about the ethics, but the concrete actions that we could take in support of those values within real-world constraints. 

I loved the way that people engaged with this premise. There were ways that people contributed that I had anticipated (such as proposing solutions that I had considered at the time I design the activity). But they were also taking the hypothetical far beyond what I had originally considered, raising questions about additional dimensions of the ethical dilemma and supporting their thinking with examples from the background readings. The conversation was lively and although I was facilitating the discussion, there was space for other people to express dissenting opinions or guide the group to explore aspects of the scenario beyond my prompts. Although we didn’t arrive at a specific decision that we should do this or that, there was some consensus that gently bending some of these rules was acceptable, especially given that it was in the interest of protecting consumers from unintentional overspending on health care. Our group felt more comfortable with not fully conforming to HIPAA if it was truly in service of the patient needs. We also discussed the ‘slippery slope’ of setting precedents, either internally or externally, about use of user data. Do we run the risk of establishing norms that give people permission to bend the rules in similar ways even if their product is not as altruistic as we belived our to be?  

Learning and Take-Aways

In presenting this to my group, I realized that there were nuances of the problem that I had not sufficiently articulated in the written briefing I created and had to supplement that with a further explanation of exactly how this use case was in conflict with existing HIPAA regulations. I also realized that aspects of how the US healthcare system functions were not common knowledge to everyone in the group. Given that just last week we had been talking about inclusion in design, I felt badly that I had been operating on an unfair assumption that the processes involved in using your health insurance, seeking care and medical billing would be familiar to everyone. A quick primer on those things would have made this discussion more inclusive. 

Finally, I designed my progression to start with a ‘warm-up’ meant to prime us to empathize with users, by putting the group in the mindset of a user, before asking them to take the perspective of a designer and then finally asking them to pivot to taking the perspective of another contributor within company. Making my intentions about each of those choices more clear or being more explicit about the connection between the first activity and the second one might have been useful to the participants.

Graphing Complexity and Autonomy with Service Slices

Part Three: Using Service Slices to understand Austin Parks Foundation users

This is Part Three in a Service Design Project for Austin Parks Foundation. For Part One: Stories from the Field, go here. For Part Two: Finding Themes, go here.

Since August, our team (Kyle, Michelle, Laura) has been working with Austin Parks Foundation to help them better understand the feelings of ownership over green spaces; specifically how those feelings of ownership can develop and drive behavior. 


Our earlier updates focused on telling the stories of people we observed through contextual inquiry and the sense-making process of theme-finding. Through these processes, we unpacked the experiences of visitors and stakeholders in Austin Parks. Storytelling led us to a heightened understanding of what was unique, evocative, and compelling about each person. 

Before moving on from those themes to insights and problem statements, we want to reexamine our data in a new way through visualizations. We call these constructions ‘Service Slices.’

IDSE101_service slices presntation graphic blog


Service slices are a tool for turning the invisible into a tangible artifact. While our research looked at 3 distinct behavior groups, we focused specifically on Park Adopters to create service slices. Park adopters are APF volunteers who take on leadership roles within their park.

Kyle and Laura constructing Service Slices
Kyle and Laura constructing Service Slices

We went back to our transcripts, line by line, and used them to create four service slices:

  1. Behavior and Information Exchange: To understand the actions and interactions of our participants, we diagramed their behaviors and the information they exchanged with others through the course of implementing improvements in their park.
  2. Power, Policy, Influence, and Emotion: We graphed the relationships of power and influence amongst the people, organizations, and policy players in each park adopter’s world. We also noted emotions that our park adopter expressed about aspects of their volunteer work. 
  3. Artifacts: We documented the physical tools and objects relevant to park adopters.
  4. Environment: Creating detailed diagrams of each of the adopted parks helped us understand any spatial dynamics at play.

The first two are invisible systems: workflows and power dynamics. We make these systems explicit and visual by building two distinct representations of these interactions. The second two are re-creations of physical systems: objects and the environment. While we have photos of their objects and environments, rebuilding these with special attention to how the participant relates to and functions within these systems allowed us to focus on the unique functions for this person, at this time, in this space.

Constructing one of Robert's Service Slices on the whiteboard
Constructing one of Robert’s Service Slices on the whiteboard
Developing Artifact and Environment Service Slices
Developing Artifact and Environment Service Slices

At each step, we noted opportunity areas that would be fruitful places to explore when considering design ideas. While we noted these opportunity areas, we will not start ideating design ideas until later stages.


Creating service slices allowed us to focus on specific dynamics that are elusive in a sea of transcripts. Questions start to emerge: How do they feel about this interaction? Are there larger policies guiding their actions? Are their behaviors different than their beliefs?

The process of making service slices was more valuable than the diagrams themselves. Being able to revisit this data through a new lens helped us further understand our participants, and patterns start to emerge that otherwise would have been difficult to distinguish. 

Service slices are incredibly complex in both content and visuals. Arriving at peak complexity and then simplifying these slices to make a functional artifact also helped us describe phenomena that developed. 

Below, we describe three park adopter’s experiences and opportunity areas that emerged while creating service slices. 


Touring a park with Rico

Rico adopted his park last year and enthusiastically embraces making positive changes despite not having a clear understanding of what pathways he should use to achieve results. He relies heavily on Austin Parks Foundation and 311 to get things done, but uses the organizations in ways that do not reliably further his goals. When trying to get dog waste stations installed at his park, he started to apply for grants–the process he thought was correct–only to be later told by “a park’s department manager” that the Watershed Department can actually provide them for free. He discovered that the City of Austin had a “warehouse full of them” and that the time he spent applying for the grant had been pointless (Line 142). 

Once he had requested the dog waste stations through the proper channels at the Austin Watershed Protection Department, he didn’t know how to follow up on the installation, so he fell back on contacting 311 repeatedly.  “I call 311 again if the report doesn’t go through to find out what happened. ‘Oh, well, this guy said that it’s been serviced,’ I’m like, ‘It can’t be serviced. We don’t even have the dog waste stations’” (Line 77). Despite the lack of information that 311 has about his problem, he reaches out to them because he doesn’t know who else to call.

He also believes that the incessant 311 calls will show PARD and APF that he is doing a good job. “[Calling 311] shows that you’re not gonna give up and quit. You’re steady. You gotta be consistent. And I think [PARD and APF] know that from the reports at 311” (Line 66). Absent evidence to support his theory of a direct line of communication between 311 and APF, Rico is optimistic that his efforts will be seen by someone and rewarded.

Once he knew that there was a warehouse full of dog waste stations, he began to wonder what else might be hiding away in warehouses. Maybe benches? “I’ll ask, ‘Can we get an extra bench? Did you see all the cleaning we did on the side?’ Maybe it’ll work. What do you think? Worth a shot, right?” (Line 140) His theory of how resources are allocated to parks is based on his efforts being seen by faceless bureaucrats who will reward him with what he needs to make improvements.

Rico spins his wheels constantly, unsure what is actually working. But, in only one year as a park adopter, he still has a fresh excitement about his duties as a park steward. He’s gotten lights and dog waste stations installed, cleaned up abandoned homeless encampments, mobilized neighbors to volunteer, solicited opinions and feedback, and is working on getting new benches and bike racks. He’s proud of what he has accomplished and not yet burnt out by the work that doesn’t yield results. “I’m learning as I go. And then I use all the resources” (Line 142).


Robert walks past an encampment in his park
Robert walks past an encampment in his park

Robert has been the steward of his park for several years — but was actually unclear if he was the official park adopter.  “I think someone, I think our neighborhood has adopted it, but I can’t be sure. Because there are a lot of programs… I think I’m supposed to be in charge of that. Is that with APF?” (Line 123)

Regardless of whether he was the official adopter, he functions as one organizing clean-ups of his park, coordinating with external groups to direct the development of the park on behalf of his neighbors. Priorities for him range from reducing the recurrence of overflowing trash piles by getting more trash cans to adding a walking path around the pond to make it more accessible and enjoyable.

Despite his inspiration to make big and small improvements in his park, Robert is easily annoyed by the many bureaucratic processes that have stymied his progress. He prefers to connect with people in person or by reaching out directly, and it a bit put off when the response he gets is, “Could you fill in the form, please?” He told us, “It seems like their connection with the public is quite automated, which keeps life simple, but it’s not a personal thing” (Line 119).

Over time Robert has become aware of the many city agencies and nonprofits that can facilitate projects in parks. He knows that Keep Austin Beautiful and Austin Parks Foundation both have tool libraries that he can borrow from for clean-ups (Line 47). He has coordinated with city employees at the Watershed Protection Department (Line 32), a city arborist (Line 72) and PARD maintenance workers (Line 113). He attends neighborhood association meetings (Line 30) and understands their role in advocating at the city level (Line 56). He communicates with the managers of an apartment complex (Line 92) and Public Storage franchise (Line 37) that are adjacent to the park. He leverages the police (Line 40) and EMS (Line 78) for support in addressing issues relating to people living in the park. He is aware of potential funding sources for park projects such as Texas Conservation Corps (Line 84) and Austin Parks Foundation (Line 48). 

Robert’s experience as a Park Adopter is one of ever-increasing complexity. As he becomes more savvy about navigating the appropriate channels for actions he wants to take, he discovers exponentially more avenues to pursue. Rather than being empowering, this broader perspective is overwhelming. He has been trying to build a trail around the pond at his park — and he knows that there are a lot of organizations he could partner with to make it a reality, but he hasn’t actually taken action to make it happen yet.

Right now even just getting a new trash can installed seems like too much effort. “Installing a new trash bin has to go through many bureaucracy layers…It’s very difficult. There’s litter everywhere and no bin.” Rather than deal with the various agencies that need to be involved to make the change, he’s willing to pay out of pocket and do the labor himself to install some cheap unofficial trash cans.  “I keep meaning to go to Lowes and just get two big plastic bins and chain them to a bench. I might do that tomorrow.” (Line 138) He hopes that the maintenance workers will service them like they do the officially sanctioned trash cans. 

The best strategy Robert has to exit this web of complexity is to rely on his mentor, Daniel. “It’s still quite difficult to navigate who to talk to. That’s the nature of dealing with large organizations, I suppose. If I hadn’t spoken to [my mentor, Daniel], I’d still be floundering because he is laser-guided.“ Robert’s serendipitous encounter with Daniel, and Daniel’s generosity with his mentorship are the pathway out of complexity that Robert needs. 


The road that Daniel wants to have removed from his park
The road that Daniel wants to have removed from his park

Daniel has been a park adopter for over a decade and when he walks through his park it shows. During our visit to the park, he knew almost every person there — giving them updates about park improvements, asking them about their families, making polite conversation. It’s clear he’s become a staple in the community.

He’s been leading a renovation project for over 12 years and over time he’s developed sophisticated methods of getting things done. Unlike Rico, our younger park adopter, Daniel rarely mentioned receiving help from Austin Parks Foundation, Keep Austin Beautiful, or 311. Instead, he’s learned how to go above these groups to interface with decision-makers directly. 

Rather than just trying to get more lights, trash cans, or benches like Rico, Daniel and his neighborhood association were thinking big: they wanted to shut down an entire road and turn it into a walking trail. A huge ask for the city, they built a case that by removing the road, they’d be protecting the creek right next to it. Because of the road’s close proximity to the creek, it had also experienced a minor collapse which meant emergency vehicles like EMS could not use it. 

“And if the city can’t use the street, then we had a much better argument to say, the rest of us don’t need it. [. . .] The city kind of bought the argument. There’s less for them to maintain.” 

Not only has Daniel become sophisticated with making his arguments for park improvements, he knows where to focus his efforts. Unlike Rico or Richard who interface with a complicated web of 311, nonprofits, neighbors, and grant forms, Daniel leverages his neighborhood association to lobby his City Representatives to enact change. 

In one instance, his group convinced the city to add sidewalks to make his park ADA compliant–  a “$600-$700,000” project. Yet despite these investments, he still feels his park is not receiving their fair share as city funds flood to parks in Circle K, or other emerging neighborhoods on the edge of the city.

“There’s a measure of resentment towards people west and southwest . . . who have absorbed a lot of resources. . . .A lot of these existing infrastructures have been really neglected . . . and we put money into these in lieu of funds that are supposed to be used in this neighborhood, but then they’re kind of somehow siphoned out to sprawl.”


When we visualized all of the levers each adopter pulled to enact change, a strong pattern emerged: 

IDSE202_01_service slice diagram 2

Over time, park adopters develop more and more resources they can use. Rico, a young adopter, has relatively entry-level connections and relies on them exclusively to make changes. Robert, an intermediately experienced adopter, has developed even more connections– so many that he’s unclear of which program owns what. He’s at peak complexity. Then we have Daniel, an experienced adopter who fully understands the landscape and is targeted in where to apply his efforts — choosing to interface with only a couple groups to help make changes.

This pattern follows the simple model of a complexity curve. Over time, park adopters gather more and more information and resources, reaching peak complexity (Robert). Then they start to truly understand their role and the programs around them, being able to navigate a complex system through simple measures (Daniel). 


IDSE202_01_service slice diagram 3

With this increased understanding also comes more autonomy and potential for gatekeeping. As a novice adopter, Rico relied on his community and nonprofits for changes and asked for feedback constantly:

“You can see the progress in the rating. I get some people here to rate the park from one to ten. ‘How clean do you think it is?’ ‘Ten.’ ‘Ten.’ ‘Ten.’ So it’s a big improvement.” (Line 86)

Meanwhile, Daniel and his neighborhood association were so sophisticated in their efforts, they were able to limit “outsider” access to the park by removing a road and parking: “There’s some concern that when we close this road, that there’s not enough parking, and this will be sort of pulling up the ladder [. . .] that it will be less welcoming to people from the outside of this neighborhood.”

Despite this concern, they are moving forward with the road removal anyway, which Daniel justified by saying:  “I don’t want to be harsh about it, but you know, we pay our taxes. We choose to live here because of the parks. We should have some first dibs on what happens in those parks.” (Line 145)

As park adopters become more sophisticated and autonomous, there is clearly huge potential for gatekeeping public spaces.


In the next few weeks, we will use our utterances, themes, and service slices to support the development of Insights and Problem Statements that will help us further define the problem space so we can start to create design ideas for APF.

Want a deeper look at our interview process and stories from the field? Check it out here

What’s Missing from Your Design Toolkit?

This last month we have been reading about problem solving, the work of designers and design processes. Although all still in the domain of theory, rather than practice, these authors are grappling with the question, “How do we do design?” Authors like Chris Pacione, Nigel Cross and Horst Rittel have defined the designer’s process in contrast with fields of mathematics, engineering, and economics, respectively. The designer’s toolkit is full of tools that enable us to leave behind the rules and traditions of scientific inquiry in exchange for a more humanized, multi-dimensional, and inclusive picture of the world. 


Pacione says today’s fundamental educational competencies are not reading, writing and arithmetic, but rather the fundamental skills of design, “creativity and innovation, critical thinking, problem solving, communication and collaboration.” Cross highlights intuition as the key differentiator between the problem solving performed by engineers and the problem solving done by designers. He quotes an engineering designer saying, “I believe in intuition. I think that’s the difference between a designer and an engineer.” Cross defines the core competencies of design as “the abilities to: “resolve ill-defined problems, adopt solution-focusing strategies, employ abductive/productive/appositional thinking, use non-verbal, graphic/spatial modeling media.”


Rittel discusses the economists’ application of classical physics in the pursuit of efficiency and the elevation of efficiency to moralistic heights within industry and government. Yet, he finds these methods falling short when applied in the social sciences or in government or societal planning. “We shall suggest that the social professions were misled somewhere along the line into assuming they could be applied scientists–that they could solve problems in the way that scientists can solve their sorts of problems.” In many ways the tools of the designer are as much about what they are not, as what they are.

Design has thus been defined in opposition to the empiricism of math and science. In advancing design as a superior method for solving societal problems, design theorists have rejected the tools of the engineer, the scientific method, statistical analysis, algorithms, and the like. The righteous justification for casting aside those problem solving tools that societies have found invaluable for centuries is in defining the types of problems these tools are well-suited to solve. Walter Reitman first categorized problems as ill-defined or well-defined in the 1960s as a means of understanding human cognition and problem solving. A well-defined problem is one with a single, definite solution state and a single, definite starting state, and a finite set of ‘legal moves’ and constraints.


Herbert Simon builds on the idea of separating problems into well-specified or ill-specified in the interest of articulating what types of problem solving are best suited to each. But rather than honoring the binary that Reitman constructed, Simon reimagines them in two critical ways. First, he discards the binary in favor of seeing ISPs and WSPs as a continuum. One problem might be more well-structured than another while neither being objectively well-structured. Second, he considers problem spaces that are fundamentally ill-structured, and yet composed of many sub-problems that are actually well-structured. 


The particular lens through which Simon is considering ISPs and WSPs is the implications for artificial intelligence to solve problems. He both limits the potential application of AI by positing that many problems commonly conceived as well-structured (such as a chess match) are actually ill-structured. Yet, problems commonly conceived as ill-structured, such as an architect designing a house, are largely composed of well-structured sub-problems. So although other theorists have built a wall between the design and scientific methodology or data, Simon’s construction of problems in which WSPs are embedded in ISPs call for problem solvers with both the empiricists’ and the designers’ toolkits. 


Richard Buchanan echos this sentiment saying, “The significance of seeking a scientific basis for design does not lie in the likelihood of reducing design to one or another of the sciences-an extension of the neo-positivist project and still presented in these terms by some design theorists. Rather, it lies in a concern to connect and integrate useful knowledge from the arts and sciences alike, but in ways that are suited to the problems and purposes of the present.” Buchanan doesn’t want to turn design into a science, but he argues that we need to thoughtfully consider where science and design intersect. The rise of design hasn’t (and shouldn’t) mean the fall of science, but for these two ways of problem solving to exist perpetually in parallel rather than in conversation with each other is a major missed opportunity. Buchanan synthesizes the work of Simon with John Dewey’s call for “new disciplines of integrative thinking.” Design work that cannot integrate with the sciences is a poorer realization of design.


We haven’t done enough to integrate that which is valuable from the empiricist tradition in modern design methodology. Buchanan tell us that interactions between designers and the scientific community are problematic. “Instead of yielding productive integrations, the result is often confusion and a breakdown of communication, with a lack of intelligent practice to carry innovative ideas into objective, concrete embodiment.” This is unsurprising given that design has been a discipline that has historically defined itself as the antithesis of science. Not to mention the continued skepticism that science and industry have of design. 

Jocelyn Wyatt recognizes the reluctance of industry leaders to embrace design methodology, saying, “Nobody wants to run an organization on feeling, intuition, and inspiration.” Wyatt’s view is ultimately that design has already arrived at the perfect intersection of integrating the rational and analytical with the creative and intuitive. Yet, she acknowledges that more organizations are structured around “conventional problem solving practices.” She posits that this may be due to fear of experiencing failure inherent to prototyping and experimentation processes.


What if instead, the explanation was that designers are still leaving too much on the table? Focusing on the strength of their own process and failing to leverage to advantages of a more quantitative understanding of the problem space?  In reflecting on this idea my classmate, Lauren and I considered the heightened value that qualitative data we had gathered in contextual inquiry had when considered in the context of quantitative data that differed from or directly contradicted the perceptions of our participants.


The gaps between what our participants perceive and what we know to be true are reliably interesting to us. How did they develop this different view of reality or “alternative facts”? We may trust that our knowledge exceeds the knowledge of the person we are observing or the disparity between our beliefs and those of our participants may lead us to question our own perceptions. In a data-gathering phase of design research, how do you respond to misrepresentations of reality? Do you accept it as a pertinent and interesting distortion or does it prompt you to interrogate your own beliefs or understanding?


There are a few ways that we have seen the perception-reality divide manifest in design research. First, the observed hypocrisy. While doing research on user behavior in public parks a participant emphatically told us that off-leash dogs were not acceptable to him or his neighbors and that the neighborhood had a strong ethos of self-policing around this particular norm. Less than half an hour later, one of his neighbors walked by with two dogs, one of which was off-leash. The two had an amicable discussion that included observing how this elder dog was inoffensively violating the off-leash rules. The strong self-policing ethos described was entirely absent. These are common. The food service worker who mentions always washing his hands before starting work, and then doesn’t wash his hands. A preschooler who describes the universe of Dora the Explorer in detail after his mom has said that he doesn’t watch any television. This inconsistency illustrates the gap between who we are and the idealized version of ourselves.

However, we aren’t always so lucky to always catch a person in these contradictions in a one- to two-hour contextual observation. How important is it to differentiate between the behavior “parents of preschoolers don’t allow screen time” and the belief “parents of preschoolers don’t think their children should be interacting with screens”? The primary resolution to this problem to to prioritize observing behavior, rather than eliciting opinions. We can ask questions about what we observe and given what we know about people’s tendency to present an idealized self, take with a grain of salt if the participant insists that we are observing something anomalous rather than routine. 


Yet, what about when the observed behavior isn’t rational for a given context? We’ll call this, the irrational behavior. This is the person who travels out of their way to visit a particular farmers’ market because they double SNAP (food stamp) benefits, when actually every farmer’s market in town offers the same deal. If we know the rules about SNAP and farmers’ markets we can identify this as an irrational behavior, and glean some interesting insights from this misconception. Otherwise, this behavior might pass as rational and we would miss the additional understanding the comes from identifying a gap between perception and reality.

As designers, we are seldom subject are experts in the fields we are designing for. There are ways that this is an asset, rather than a liability. Familiarity with the subject area means familiarity with a set of beliefs and judgments that may limit innovation and creativity. “This is how it’s always been done.” “This is the right way; this is the wrong way.” Additionally, it’s not realistic or an effective use of time to become a subject matter expert in each industry in which a designer works. Absent subject area expertise, how can designers increase their knowledge base to further develop their ability to spot pertinent and interesting gaps between perception and reality?


In the field of remote sensing and GIS, practitioners talk about “ground data.” When I did research using satellite imagery and GIS to identify areas of reforested and old-growth forests in the Pacific Northwest, I couldn’t just rely on the images, I needed ground data. Real-world points of reference where I knew that the areas that I was seeing in my satellite data were known to be either reforested or old-growth. Using these known areas as a baseline, I could analyze the properties of those areas of the imagery and use correlation to identify which other areas on the map likely shared a common history of being logged or pristine.

How can designers employ ground data in their work? In the example of the farmers’ market, ground data might come in the form of existing knowledge, or from additional research. How can we cultivate data pertinent to our areas of research and integrate them into our qualitative research processes? I’m not suggesting that designers become statisticians or scientists, but advocating for the integrative approach advanced by Simon and Buchanan. If we understand the problems we are solving to be complex enough to contain both well-structured problems and ill-structured problems, then surely some of the data or tools of the sciences can advance our understanding of problems in meaningful and actionable ways, particularly the well-structured components of complex problems. 


My recent parks research provides a potential example of the intersection of design research and quantitative data. While talking to a participant who works in parks she emphatically described the ways that her organization seeks to approach their work with an equity mindset. She was thoughtfully aware of the history of racial inequality in Austin and the ways that had manifested in parks. Yet, this seemed like a possible example of an observed hypocrisy, as the methods that the organization employs to direct resources are subjective and thus may reflect misconceptions or blindspots despite the best of intentions. Further, the process seems highly vulnerable to “squeaky wheel” bias that might favor those with the means and agency to advocate for themselves.


As a design researcher I wanted ground data to validate or invalidate the claim that the organization was achieving the equity outcomes that are part of their mission. If their impact and their intentions were not aligned, this would be a fruitful problem space to explore, or it would not be a problem at all if the organization was effectively achieving their equity goals. I could ask more park users about their perceptions of equity (which I did). This gave me important and valid data about users’ perceptions of equity (they didn’t think funding was equitable). But I still wanted to know if we made a perception problem to solve or a systems problem to solve or both.


Serendipitously we ended up talking to another person who works in parks who had a shared passion for equity and concern about resource allocation in parks. She shared with us a tool that she uses to map potential investments in Austin parks. She told us, “It has all these different layers. You can turn on a master layer that puts together an aggregate layer of things like low income, low food access, high obesity, high chronic disease–all these like high need things–children under eighteen, low socioeconomic status, all of that coming together.” I could map the recent investments made by the organization using subjective methodology to allocate funding and see how those correlated with the empirically identified areas of high need in Austin. That would be great ground data to validate the organizations claims.

Where does this fit into my current framework for design research? The way our research process is organized looks like this:



The first two (contextual research, themes) are focused on the processes of collecting and organizing perceptions. The latter two incorporate the designers intuition and knowledge to make meaning of the first two. I would argue the space between theming and insight formation is the place to apply quantitative data. Adding different types of data at this stage fortifies the designer to approach the formation of insights and design ideas from a stronger vantage point.


The highly regarded philosopher of design and creativity, Edward de Bono, advocates literally putting on different hats when participating in creative work. A hat for criticism and skepticism, a hat for optimism and “blue sky” thinking, one for intuition and emotion, one for provocation and deviant thinking, and so on. Yet, none of the hats on his hat rack is a statistics, data and science hat. Perhaps because this lens is commonly seen as a creativity killer. 


People may believe quantitative data is reductive, dry, lacks personality or nuance. But when I hear those critiques, I think, ‘You’re just looking at the wrong data!” The right quantitative data for your problem will spark curiosity, will express nuance, will prompt expansive thinking. With practice interpreting or visualizing data, quantitative data can tell a lively and highly specific story, or at least point you in the direction of one. If my overlay of the high-need GIS data with the non-profit’s recent project sites shows neglected areas of high need, I can explore why. I can visit those parks and talk to neighbors their to get a fuller picture of what is happening across the city outside of my convenience sample.


First, to fully exploit the value of a data-informed design practice first thing we need to do as designers is to let go of the idea that design is defined in opposition to science. While it is true that design methodology is distinct from scientific methodology, positioning these two disciplines at odds unduly influences designers to abandon both the tools and the products of scientific inquiry. While your design toolkit is powerful without any scientific resources, it is only more powerful when you are able to thoughtfully incorporate scientifically derived data or methods.


Second, we need to examine the design tools and methodologies that we rely on and consider how, when and where we might integrate empirical data. A choice to incorporate it at the beginning of the process might limit building empathy and understanding the problem through your users eyes. At the end might be too late to make use of the data. Wyatt describes three phases of design, “inspiration, ideation, and implementation.” Within this model, the most effective place to employ empirical data is somewhere within the ideation phase. Critically examine your design research process and consider where quantitative data best fits.


Finally, just as Pacione makes a case for basic design literacy for everyone, designers need to embrace basic data literacy. A data-informed designer is knowledgeable about what types of quantitative data sources are publicly available and what types of quantitative data your clients likely have access to. She also has at least basic competency in techniques for quantitative data gathering and processing. Data literacy requires being able to interpret and visualize quantitative data sets and identify bad or unreliable data sources. With practice, thinking with your statistics, data and science hat will become second nature, and the results of your design research will be even more persuasive and powerful.


Meditations on Capitalism, Poverty and Global Markets

In the past few weeks of reading about the codification of social entrepreneurship as a practice, we’ve considered the benefits and challenges of structuring an organization in that way. And we’ve contrasted the social entrepreneur’s approach with other models, such as large NGOs, small non-profits, public-private partnerships or for-profit businesses that target people in poverty (or euphemistically, ‘the bottom of the pyramid’).


As anyone who has been in class with me the last few weeks knows, I form decisive opinions easily. And while these opinions are both heartfelt and subject to change from day to day, the speed at which we arrive at opinions has me questioning the value of these snap judgements.


It felt easy to criticize firms strategizing about ways to take one more dollar, taka or naira from the poorest people on Earth by selling them products of dubious value. It felt easy to reject the hubris of saviors from wealthy countries flooding into poorer communities with their fancy degrees, rich donors and profoundly unsophisticated understanding of the problems they were trying to solve. It felt easy to identify the short-comings of social business models that maintain the agency of the entrepreneur and disregard the agency of the marginalized people their companies are meant to benefit.


But are those productive outcomes?


There’s something satisfying about arriving at a judgment. This thing is right, that thing is wrong, case closed. Having an opinion usually feels pretty empowering. An opinion can’t really be wrong, and having one grants you entry into the discussion. But, what do we bypass when we leap to judgment rather than sit with the ideas for longer periods of time? 


These readings are meant to give us grounding and context for the design work we do. They aren’t defining a problem space and giving us marching orders for us to take our six weeks of design training and go solve global poverty and inequality. Solving these problems is definitely beyond the scope of our class. In fact, even formulating a definite answer about best practices or frameworks would be going too far given a fairly cursory literature review.


What exists between an answer and an opinion? It’s understanding. This week instead of creating an artifact that had definitive content, answers, recommendations and opinions, I wanted to create space for reflection that might lead to understanding. For existing in the liminal space of knowing that you don’t know. For giving up the unearned confidence that having an opinion bestows and instead hold on to not knowing, wondering, and pondering.


Opinions form quickly. 


Understanding takes time.

panel-1-01 panel-2-01 panel-3-01 panel-4-01 panel-5-01 Panel-6-01 Panel-7-01 Panel-8-01 Panel-9-01 panel-10-01 panel-11-01 panel-12-01 panel-13-01 panel-14-01 Panel-15-01 Panel-15b-01 panel-16-01 Panel-17-01 panel-18-01 panel-19-01 panel-20-01 Panel-21-01 Panel-22-01 Panel-23-01 panel-24-01 Panel-25-01 Panel-26-01 Panel-27-01 panel-28-01 panel-29-01 panel-30-01