Concept Graph & Resume using Claude 3.5 Sonnet | Chat GPT4o | Llama 3:
Resume:
1.- Market design: Studying markets as algorithms that interact with individuals to produce or allocate scarce resources.
2.- Societal value maximization: Focusing on markets that aim to maximize societal value rather than generate revenue.
3.- Money as a tool: Using money in market design to measure value and incentivize truthful reporting, even for non-profit goals.
4.- Ascending auctions: A method to allocate items to highest-value buyers by increasing prices until only one bidder remains.
5.- Markets without money: Exploring alternatives to monetary incentives in markets where using money is infeasible or unethical.
6.- Congestion as incentive: Using traffic congestion instead of tolls to route vehicles efficiently in road networks.
7.- Virtual rewards: Online platforms using badges, leaderboards, and other non-monetary incentives to encourage user contributions.
8.- Badge systems: Rewarding users with virtual badges when their contributions reach certain thresholds.
9.- Leaderboards: Ranking users based on their contributions to incentivize competition and increased participation.
10.- Optimizing virtual reward systems: Designing badge and leaderboard systems to maximize user contributions.
11.- All-pay auctions: Drawing parallels between virtual reward systems and all-pay auctions to optimize contribution incentives.
12.- Voting systems: Using votes instead of money to elect leaders that maximize societal value.
13.- Quadratic voting: A voting system where voters buy votes with money, paying quadratically more for additional votes.
14.- Point-based voting: Allocating points to voters to spend on candidates, creating trade-offs and revealing preference strengths.
15.- School choice mechanisms: Designing algorithms to assign students to schools based on preferences and priorities.
16.- Pareto efficiency: Seeking allocations where no student can be made better off without making another worse off.
17.- Deferred acceptance algorithm: An iterative method for matching students to schools based on preferences and priorities.
18.- Boston mechanism: A school choice algorithm where students are irrevocably accepted to schools in preference order.
19.- Single ticket raffle: A simplified version of the Boston mechanism using tickets to reveal strength of preferences.
20.- Risk as a metric: Using willingness to take risks as a way to measure preference strength in allocation mechanisms.
21.- Approximately optimal mechanisms: Showing that certain non-monetary mechanisms can achieve near-optimal outcomes in various settings.
22.- Fairness in allocation: Considering how to design mechanisms that are perceived as fair by participants.
23.- Simplicity in mechanism design: Striving for algorithms that are easy for participants to understand and use.
24.- Ordinal vs. cardinal preferences: Distinguishing between mechanisms that only use ranking information and those using preference strengths.
25.- Random tie-breaking: Using randomization to resolve ties in priority-based allocation systems.
26.- Expressing preference strength: Designing mechanisms that allow participants to indicate how strongly they prefer certain outcomes.
27.- Intra-personal utility comparisons: Focusing on comparing preferences within individuals rather than across individuals.
28.- Mechanism design without money: Applying mechanism design principles to settings where monetary transfers are not allowed.
29.- Alternatives to money: Using scarcity, risk, fame, and time as substitutes for money in mechanism design.
30.- Applications of non-monetary mechanisms: Exploring uses in public housing, online dating, social networks, and personal data markets.
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