This studio was about taking human decisions and solving them using computer friendly algorithms. This was often counter-intuitive, making simple to solve problems into processes with several steps and variables. We wanted to make them thorough so they considered all factors, and we wanted to prompt the user for info with a simple and user-friendly website. 

The issue we’ve tried to address is that it is very difficult to find restaurants to eat at when one has multiple or severe allergies. We decided to create a website that takes in a list of allergens to avoid, as well as cuisine types that the user prefers, and returns a list of restaurants in order of most to least likely to work. To do this it takes into account ingredients lists for individual dishes, as well as kitchen contaminations for more severe allergies. It also considers type of food and Yelp rating.

We designed a simple input system for restaurants that could be sent to them en masse, allowing us to uptake and update our restaurant data more effectively than alternatives like AllergyEats that rely on users submitting data. This strategy of 100% user-submitted information works well for major chains like McDonalds, but fails to give users information for smaller restaurants. We didn’t have time to code this restaurant-facing interface, but we wrote a proof-of-concept for the restaurant ranking which is entirely functional. We also wanted to add options for different types of food, selectable with checkboxes. This would make our algorithm more complex, but we’ve included it in our mockups.

The goal of the studio was to create a website that would act as a decision aid to help users answer complicated questions. 

Often times people are unable to decide what they should eat that is nutritious and healthy for them. We created a website that acts as a decision aid to help you decide what to eat as a healthy meal or snack based on your days previous meals and the recommended daily value of different nutrients.

In order to offer food suggestions, our website takes information from three sources: 1. Sliders that allow the user to indicate approximately what types of nutrients they have consumed. 2. A checkbox that indicates what meal they wish to eat next and hence how many nutrients they need from the food suggestions. 3. A database of food items with their associated nutrients. The nutrients our website considers is protein, fiber, sugar, fat, sodium vitamin A(IU), vitamin c, calcium, iron, magnesium, and potassium. The decision making algorithm starts by computing the remaining nutrients needed by computing the difference between the amount of nutrients they have consumed and the amount of nutrients they need for the next meal. Once this difference is found a search starts which explores a branching tree. Each branch of the tree represents a food item from the database that the user could consume. Each node in the tree represents the remaining nutrients they need to eat. The search stops when the remaining nutrients at a particular node is negative, indicating they have eaten enough. The returned answer is the set of foods associated with the branching path from the root of the tree to the node with zero remaining nutrients.

The goal of the studio was to create a website that would act as a decision aid to help users answer complicated questions. 

Often times people are unable to decide what they should eat that is nutritious and healthy for them. We created a website that acts as a decision aid to help you decide what to eat as a healthy meal or snack based on your days previous meals and the recommended daily value of different nutrients.

In order to offer food suggestions, our website takes information from three sources: 1. Sliders that allow the user to indicate approximately what types of nutrients they have consumed. 2. A checkbox that indicates what meal they wish to eat next and hence how many nutrients they need from the food suggestions. 3. A database of food items with their associated nutrients. The nutrients our website considers is protein, fiber, sugar, fat, sodium vitamin A(IU), vitamin c, calcium, iron, magnesium, and potassium. The decision making algorithm starts by computing the remaining nutrients needed by computing the difference between the amount of nutrients they have consumed and the amount of nutrients they need for the next meal. Once this difference is found a search starts which explores a branching tree. Each branch of the tree represents a food item from the database that the user could consume. Each node in the tree represents the remaining nutrients they need to eat. The search stops when the remaining nutrients at a particular node is negative, indicating they have eaten enough. The returned answer is the set of foods associated with the branching path from the root of the tree to the node with zero remaining nutrients.

This studio was based around decision making, and utilizing it in the form of a website. We had to first study flowcharts and different means of helping people make decisions for inspiration to make a website that assists others in making them. People have no Idea what hobby or job they want to take up. This website will use a keyword forum searching system to help each individual person using the website find their new interest using their old interests. My website pretty much uses keywords to help you make the decision of what you want to do with your life. This can be large scale such as a job or not, and just help you find a hobby. Instead of making some website that is like one of those Facebook quizzes I wanted to make something that felt more like a legit website than some quiz to pass time. People can preset things they are interested with their account, then the website uses these keywords to link them to forum posts with people either suggestions jobs or hobbies, or offering jobs. This website uses a forums homepage which will have all of the top posts of the week that relate to their own keywords so each week they can find new interesting things to try out. There is also creating the account where the user, as I said before, can enter in things they have done from school or previous hobbies or things they are interested in. each user can also make a post which they enter in keywords, so they match up and help people find their posts. The development of my project went from me spending a day fleshing out and really planning what I wanted to do to me wasting a day because I had no Idea what I wanted to do, which is pretty ironic because we were in the decision making studio. Next I had to learn coding which was very difficult for me, but I had a lot of help from David and mainly did hands on learning, which is learning how to code while I was working on my website.

One of my biggest challenges was finding out how my website was going to work because I really wanted it to be different. I had to spend a lot of time just looking at a wall and planning what I needed to do, because that is really the only way I can come up with a good ideas. I also did a bit of active thinking while drawing, which helped. I used other websites, some that I use and some that I don’t as an inspiration for my website even though they really had to nothing to do with it. It took a while but I finally had a good president for my website that I really thought would work.

My next challenge was running into all of my limitations either because my ideas were unrealistic or because I did not have the time or abilities to make them possible. these limitations were a lot of me wanting to make my website as good and as useful as some of the other websites I was thinking about when planning my own.

My last real challenge, which was also the biggest, was doing the coding by myself. I had help from David which was very nice, but I still had to dive into this unknown world and make a  website layout. Most of the time I barely knew what I was doing and If I am being completely honest I'm not sure If I could do it again right now, but I really enjoyed the experience and would one day like to try and code again, but be a lot better at it and have more time to work at it. I somehow got it done because one day I buckled down and really focused with barely any distractions. Luckily David was alway there to help me when I got confused.

This studio was about taking human decisions and solving them using computer friendly algorithms. This was often counter-intuitive, making simple to solve problems into processes with several steps and variables. We wanted to make them thorough so they considered all factors, and we wanted to prompt the user for info with a simple and user-friendly website. 

The issue we’ve tried to address is that it is very difficult to find restaurants to eat at when one has multiple or severe allergies. We decided to create a website that takes in a list of allergens to avoid, as well as cuisine types that the user prefers, and returns a list of restaurants in order of most to least likely to work. To do this it takes into account ingredients lists for individual dishes, as well as kitchen contaminations for more severe allergies. It also considers type of food and Yelp rating.

At the start of our brainstorming process, we looked at existing websites that try to help people with dietary restrictions find food. We also decided which data points we wanted to factor into in our decision. We then discussed priorities; for example, avoiding deadly allergens is more important than great customer service, so a restaurant’s rating should not be our top priority like on a site like Yelp. We skipped ahead after that to start the page layout so we could visualize the product before going into the fine details. 

Then, we moved on to the algorithm. This was the hardest part, as we realized there were many more factors that we had not accounted for (ingredients lists do not necessarily account for synonyms or allergens, restaurants may introduce new ingredients so the customer should always call ahead). The proper way to represent the complex relationships between the huge number of ingredients used in food is with a semantic graph database, a huge web of ingredient names with relationships between different ingredients being categorized. 

This would let our software know the difference between, for example, “yogurt shake” (contains dairy) and “coconut milk yogurt” (dairy-free). To simplify this process, we eventually decided to use a point based suggestion system. Our system searches for all user-submitted allergens and their synonyms in every meal for restaurants nearby and assigns the restaurant a point for each collision between a potential allergen and an ingredient on the menu. We then display the restaurants in order of most likely to least likely to work for the user. 

We designed a simple input system for restaurants that could be sent to them en masse, allowing us to uptake and update our restaurant data more effectively than alternatives like AllergyEats that rely on users submitting data. This strategy of 100% user-submitted information works well for major chains like McDonalds, but fails to give users information for smaller restaurants. We didn’t have time to code this restaurant-facing interface, but we wrote a proof-of-concept for the restaurant ranking which is entirely functional. We also wanted to add options for different types of food, selectable with checkboxes. This would make our algorithm more complex, but we’ve included it in our mockups.

The decisions we make define our personalities, can affect our lives, and can change the world. We make hundreds of decisions a day. But, how do we actually make decisions? How do we know we've made the right decision? And, does it matter?

In this studio, students will gain a deeper understanding of how we make decisions. They will learn how information impacts our decisions and how we can represent that information in useful ways. In particular, students will learn about decision diagrams, search trees, shortest path algorithms, utility functions, optimization, and probabilities. They will use these skills to design a web-based decision aid: a website that uses databases and/or collected information to help visitors make particular decisions.