Eddy | Devpost

Opening screen
Enter topic for making the decision
Enter attributes used to evaluate options
Enter the options and attribute scores
Your options are evaluated!

Inspiration

It's really hard to make decisions sometimes. Our friend Eddy knows this struggle all too well. When there's too many options to choose from, and too many factors to consider, we suffer from paralysis by analysis.

So inspired by the latest psychological research into decision making, we designed this app to make decisions simple.

What it does

Eddy is a quantitative decision making app which tells you the best choice to make based on what's most important to you.

Users input the options available to them, the attributes they need to factor into making a decision, and assign scores for each attribute for each option. Then, our algorithm evaluates the suitability of each option based on these weighted attributes. This information empowers the user to make the best decision for their situation.

How we built it

We built the app using the Kivy library in Python, which allows us to easily run it on both Android and iOS. It uses a simple state machine which transitions from the menu screen, to the user's selection of topics, attributes priorities, and entering of options, to the program's decisions, and finally returns to the menu screen.

We experimented with variations of the choice evaluation algorithm to find the optimum method to appropriately weight the attribute priorities. The current algorithm divides the weightings on a linear scale between 1 and 2. For example, if there are 3 attributes, the weightings will be [2, 1.5, 1].

Challenges we ran into

Learning and working with Kivy during the Hackathon. The library was new to the team and had very little examples online. There were many paths to achieving the same outcome with the library, and so there was a large learning curve in figuring out the easiest option to implement in the given time frame.
Devising an algorithm to ensure factors are weighted reasonably when calculating an ultimate score to rank options based on user priorities.

Accomplishments that we're proud of

Reaching our minimum viable product which embodies a majority of the functionalities and design of the app in a short time frame, especially with a library that our team has not encountered before.

What we learned

How to design a user-focussed app to reduce analysis paralysis, a common problem given the number of choices we have in modern society.
How to effectively work as a team in developing and pitching the app, given that this particular app had no modularity - something to take into consideration in our future development.

What's next for Eddy

More features to enhance user experience! For example, ranking significance of attributes using a drag feature and ability to save and edit decision attributes.
Introduce social component to making decisions. To boost user engagement and allow users to learn from others, we will add a community page to the app. Here, users will be able to share their topics, and the attribute groups they decided to use. It will be easier than ever to come up with attributes, and users can be connected to a community of people facing similar choices.
Premium version, with updated algorithms to suit more complicated attribute analysis. This would help larger organisations inspire more confidence in decisions and make factors which go into decisions more transparent across the workplace. Ultimately, a company’s value is just the sum of the decisions it makes and executes. According to the McKinsey Global Survey on average, 61 percent say most of their decision-making time is used ineffectively. For managers at an average Fortune 500 company, this could translate into more than 530,000 days of lost working time and roughly $250 million of wasted labour costs per year. The knowledge work is a powerful sector of the economy.