Resource Allocation with Karma Mechanisms—A Review
Abstract
:1. Introduction
2. Methodology
2.1. Literature Review with Systematic Mapping Study
2.2. Research Protocol
2.2.1. Protocol Planning
- How did Karma evolve in the literature over time?
- What is the intellectual field-structure of the Karma citing literature corpus?
- What is Karma defined as and which relevant terminology exists in these fields?
- Which concepts as an alternative to Karma exists in these fields?
- How is Karma different from other resource allocation mechanisms?
- How is Karma applied as resource allocation mechanism?
- What are the parameters & options when designing Karma applications?
- How to model and formally describe the Karma mechanism?
- What is a rational policy in a Karma game?
- Which features does the Karma mechanism provide?
- What are promising future research questions for Karma?
2.2.2. Literature Retrieval
2.2.3. Study Selection and Quality Assessment
- (i)
- download the scientific publication as a PDF document from the download link
- (ii)
- download their bibliography meta-data as a Bibtex file from Google Scholar
- (iii)
- extract the full text from the PDF files into TXT files for further processing, and
- (iv)
- determined how often Karma was mentioned in the papers by counting the term “Karma”.
2.2.4. Classification and Analysis
2.2.5. Mapping
2.3. Topic Modeling with Latent Dirichlet Allocation
3. Review of the Karma Literature
3.1. The Origins of Karma
3.2. Two Decades of Karma
3.3. Karma Modeled as a Game
- has a specific amount of Karma
- has a random, time-varying urgency (represents the agent’s cost when not obtaining a specific resource)
- has an individual temporal consumption preference type (discount factor, represents the subjective trade-off between consuming now versus later)
4. Karma Resource Allocation and Mechanism Design
4.1. Applications of Karma as a Resource Allocation Mechanism
4.2. Karma Mechanism Design Framework with Parameters and Options
- The parity represents the relationship between Karma and the resource. The parity could be a price, meaning that resources can be traded for different amounts of Karma. This can be useful in case of non-homogeneous resources, e.g., a large file can be exchanged against three small files. The parity could also be binary, meaning that one resource can be traded for exactly one unit of Karma. This can be useful for homogeneous, atomic resources, e.g., one evening of babysitting service. The parity could also be a threshold, meaning that an agent needs a certain amount of Karma to be eligible to consume resources.
- The balance limits represent the limitations of Karma ownership. Either, agents could have unlimited amounts of Karma, or there could be restrictions towards upper bounds to avoid hoarding. Depending on the resource allocation mechanism, the absence of lower bounds could also enable an agent to have Karma debts to a certain amount.
- The amount control represents the monetary control mechanism to control the total amount of Karma circulating in the system. Either it could be controlled by the system itself, in order to keep the amount of Karma per capita at specific levels. It could also just be constant or not controlled at all. Besides, Karma points could also expire or depreciate.
- The initialization represents how agents are initially provisioned with Karma. This could mean at the beginning of the establishment of a Karma mechanism, or dynamically for new joining agents. This could either happen by an equal, or random initial endowment, or not at all, meaning that agents need to provide some time before they can consume. Prendergast (2022) even used a weighted initial endowment according to the central coordinators’ perception of need.
- The redistribution represents schemes of how Karma is redistributed across agents. It could be, that after each period, Karma is distributed via taxation of property. It could also be redistributed by taxation of payments, via a lottery, or there could be no redistribution at all.
- The price control represents how prices for resources are determined. Prices could be determined by market mechanisms such as auctions, where forms of bidding processes take place with varying degrees of information transparency. Besides, prices could be determined by a central coordinating authority (the system), or not determined at all (in the case of binary parity).
- The price limits represent the limitations of resource prices. Prices could be not limited at all, meaning also negative prices could be possible leading to earning Karma through consumption (e.g., goods with negative utilities). Prices could also only be positive, defined by rational behavior, or price limits could be fixed (in the case of binary parity).
- The resource provision represents who is the resource provider. Either agents could provide resources, or the network (system) itself.
- The resource allocation represents how a decision is made about whom to provide the resource to. Depending on the price control, it could be the highest or the second highest bidder who receives the resource; it could also be a trading and exchange system that executes orders such as for double actions. In cases, where not a large number of agents, but only a pair of two agents interact, the resource provider could assess if the bid is high enough the effort. Besides, if the resource is provided by a central system or network, it could always be provided to the agent, in case it accepts the system defined price.
- The counter-party represents with whom an agent interacts. It could be that the agent interacts with (all) other agents in case of a market (auction). Besides, it could also be that the agent just interacts with exactly one other agent, or with no other agent but the system itself.
- The peer selection represents how agents find their peers for an interaction. It could be that agents find (all) other peers through the market, so they do not decide actively for peers. Similarly, the neighborhood or a recommending/guidance system could provide a subset of the market to compete with. It could also be, that agents are randomly assigned through each other, e.g., meeting at an intersection. And finally, it could be actively selecting peers.
- The decision-making represents if agents are free to make their decisions. It is important to emphasize, that in the Karma mechanism, the buyers and sellers are always free to make their decisions (neglecting their urgency and needs).
- The urgency process represents how the urgency of agents emerges over time. It could be that all agents share a similar (homogeneous) or a different (heterogeneous) urgency process, with similar or different probabilities for different levels of need. Moreover, it could be that the urgency at a later point in time depends on previous resource allocation, e.g., starvation or dehydration.
- The temporal preference represents how agents prefer present over future consumption. It could be that all agents share a similar temporal consumption preference (homogeneous), or that they differ (heterogeneous).
- The payment amount represents what a buyer (agent) needs to pay in case it obtains the resource allocated. Depending on the price control and other design parameters, there are many payment rules possible. The buyer could pay the bid, the bid of the peer, the difference between its and its peer’s bid, a fixed price (in case of binary parity), an ordered amount by the system, or even nothing.
- The payment receiver represents who receives the Karma paid by the buying agent. It could be, that the resource-providing agent receives the payment, or that the payment is equally distributed across the population of agents, or weighted distributed according to how much Karma the population has, or to the system.
- The Karma gain represents how agents can earn their Karma units. Agents could earn Karma by providing resources; in case of negative prices, it could also happen by consumption.
- The Karma loss represents how agents can lose their Karma units. Agents could lose Karma by resource consumption or expiry. Besides, agents could also lose Karma by untrustworthy, rule-violating behavior.
4.3. Karma Mechanism Design at the Example of Wi-Fi Sharing
4.4. Other Applications of Karma
5. Future Research Directions
6. Concluding Remarks
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Tables
Ref. | Context Summary | Description and Problem |
---|---|---|
P2P and Cloud Networks | ||
Garcia and Hoepman (2004); Vishnumurthy (2008); Vishnumurthy et al. (2003) | Title: Resource sharing in peer-to-peer networks Examples: Filesharing like GnuTella, BitTorrent, Napster, eDonkey2000 Agent: User/participant Resource: Resources provided by other agents (such as files) | Agents can share resources with other agents by consuming (=downloading) and providing (=uploading). Problem: Lots of users just consume, but do not contribute resources. |
Oliveira et al. (2011) | Title: P2P network of (home) computers Examples: BOINC, SETI@home, GINGER Agent: Computing machine Resource: Computation cycles (to calculate a certain result from executing a computer program) | Agents can offer their idle time to execute jobs (depending on requirements such as OS, available software, max. RAM and CPU…) and execute jobs on other machines when needed. Problem: The decision where one or multiple jobs are executed, and how to prioritize jobs in case there are too many jobs and too few idle machines (that fulfill the requirements) at the same time. |
Mitra and Maheswaran (2005) | Title: P2P public-resource management of computing utilities in Galaxy Network Examples: BOINC, SETI@home Agent: computing machines, in the roles of resource participant (RP) and resource broker (RB) Resource: Computation cycles (capacity shares) | Computing machines, such as local research clusters, need to execute computing jobs on demand, for the rest of the time they are idle. Being connected to the proposed Galaxy network enables them to speed up their own jobs by using the idle times of other agents, but also requires them to offer their idle time to others. Problem: Computing machines leave the Galaxy network when they do not see the benefit of sharing their resources during idle times. |
Telecommunication Networks | ||
Shen et al. (2014) | Title: Distributed small cell network for mobile communication Examples: 5G, LTE Agent: User equipment (mobile cell phone, UE) Resource: “Silence of others”, others not being connected to neighboring small cells in case one agent is at the intersection of two cells for a specific amount of time | Agents can connect to one small cell, then call, and move around; when they leave one cell they just enter the next one and can use the system again. Problem: Downlink inter-cell interference problem = when the agent is at the intersection of two cells, and experiences interference with an active neighboring cell; therefore, its signal-to-noise ratio SNR drops significantly and causes an outage in the call; turning off the neighboring cell to mitigate the problem can yet cause the outage for other agents that could be in that cell at that time. |
Efstathiou (2006) | Title: Shared wireless local area network Examples: HotSpot, Connectify Agent: A mobile user in need of internet, who is member of the system Resource: WLAN access and use | WLANs are increasingly installed everywhere and usually cover greater areas than intended by installers. Mobile users that are members, can use WLANs offered by other members, instead of mobile data to have higher speed and less mobile data consumption. Problem: Members can not always consume, they also need to provide for others in order for the system to work. |
Buttyán and Hubaux (2001, 2003); Mastronarde et al. (2015); Xu and van der Schaar (2013) | Title: Mobile (ad-hoc), Wireless Relay Networks Examples: LTE, 5G Agent: A mobile user in need of transmission of a package but with no direct signal to a base station (such as from a cellular network) Resource: Relay transmission service | Agents sometimes need to use the internet on demand while being mobile, but due to their movement sometimes have no direct connection to a mobile internet base station. However, other agents can share internet access by forwarding (transceiving) packages forming an indirect signal path and making the internet more accessible. This forwarding by other agents is called relay transmission. Problem: Offering relay transmission for others is causing agents costs, such as battery drain and reduction in bandwidth. |
Road Transportation Networks | ||
Pedroso et al. (2023); van de Sanden (2022) | Title: Toll-pricing in mesoscopic transportation networks of two nodes and n arcs (parallel-arc network) Examples: three possible ways of travel from A to B but have different travel times such as different modalities like car, bike, public transport Agent: Commuter Resource: Capacities of different ways of traveling | Commuters can travel (or stay at home) using different ways of transportation that offer different comfort. For each way of transportation, they need to pay a price in Karma points. The prices are defined by a central controller that learns data-driven how to global-optimally design prices. Problem: Commuters can travel (or stay at home) using different ways of transportation that offer a different comfort. For each way of transportation, they need to pay a price in Karma points. The prices are defined by a central controller that learns data-driven how to global-optimally design prices. |
Censi et al. (2019); Elokda et al. (2023) | Title: Intersection management through coordination mechanism in road networks Examples: road intersection Agent: Vehicle Resource: The right to pass the intersection first | Vehicles drive in a road network and pass intersections. In case two vehicles meet at an intersection at the same time, they need to coordinate themselves on which vehicle can pass first. Vehicles have different urgencies depending on external factors. Problem: How to coordinate which vehicle can pass first. |
Elokda et al. (2023, 2022) | Title: Traffic congestion management with priority lanes for rush hours Examples: roads to enter cities, and shared by lots of commuters with similar origins and destinations Agent: Driving commuter Resource: The usage of priority lanes | Commuters use a road with two lanes. On a daily base, commuters can either use a free lane, which is usually slower due to congestion or use a priority lane and pay a certain fee to travel faster. Problem: The morning commute problem, which describes the phenomenon that many commuters share similar origins, destinations and preferred times of arrival, which can cause traffic congestion on roads with limited capacity (bottleneck). |
Ref. | Context Summary | Description and Problem |
---|---|---|
Social Networks | ||
Prendergast (2022) | Title: Donation allocation across a network of food banks Examples: FeedAmerica, Tafel Agent: Food bank Resource: Food donations, truckloads | FoodBanks distribute food to the poor in society and have many different sources of donations. One major supplier of food banks in the USA is FeedAmerica. FeedAmerica is a large donation-collecting platform with warehouses and logistics, that forwards the donations to foodbanks. Foodbanks use an auction mechanism to bid for truckloads depending on the need and type of truckload. It is also possible that food banks donate food to FeedAmerica which has an oversupply of stock. Problem: Before FeedAmerica introduced the auction system, resource allocation was conducted in a centrally planned manner; however, the dynamics of the needs of single food banks, what food banks had on stock locally, and which other sources food banks had available, were impossible to be taken into account. |
Johnson et al. (2014) | Title: Babysitting Co-Op of the Capitol Hill area in Washington on a network of 150 married couples Examples: Capitol Hill Agent: Married couple with baby Resource: Babysitting service for one evening | Couples with a baby need babysitting services from time to time. They can pay a scrip to another couple to babysit for them, respectively earn a scrip for babysitting another couple’s baby for one evening. Problem: Find a babysitter when necessary. |
Kim et al. (2021); Roth et al. (2004); Sönmez et al. (2020) | Title: Living-organ donation allocation between incompatible donor-patient pairs Examples: KidneyExchange Agent: Incompatible, living donor-patient pairs Resource: Patient-compatible organ donation | Living-organ donations are donations of organs from a healthy individual to a person (usually a friend or family). Usually, healthy individuals are just ready to donate organs to a person they know and care about. Unfortunately, there are many medical incompatibilities in donating organs. However, patients can exchange the offering of their friends to donate their organ to another, compatible patient, and in return receive the top position on the waiting list for cadaver donations (or the living donation from another suitable couple) Problem: There are too few available organ donations (both living and cadaver). The living-organ donations could complement the number of cadaver donations, but the readiness of living-organ donations is usually just for relatives, friends, and family. Medical incompatibilities of organs impede living-organ donations. |
Gale and Shapley (1962); Hylland and Zeckhauser (1979) | Title: Individuals obtaining admission to positions Examples: College admissions, job offers, PromNight assignment, village marriage problem Agent: Individual with admission Resource: (Individually) preferred positions | N individuals need to be assigned to N positions. Individuals have a preference over which positions they prefer. Individuals obtain positions, but before accepting them, they can exchange them with other individuals first. This exchange can be repeated several times. Depending on the model, it can also be not the position itself, but the place on a waiting list or the probability to obtain a position that can be exchanged. Problem: The preferences of individuals are usually unknown to a central assignment authority. Even if they are known, the assignment can become computationally expensive and complicated. |
Ref. | Src. | System Goal | Agent Goal | Agent Actions |
---|---|---|---|---|
P2P and Cloud Networks | ||||
Garcia and Hoepman (2004); Vishnumurthy (2008); Vishnumurthy et al. (2003) | P | Agents have a great user experience and lots of available content on demand relevant to them | Consume as many resources as necessary when desired | - Provide resources - Consume resources |
Oliveira et al. (2011) | P | Agents have a great user experience and have their jobs executed as fast as possible when they need it | Get computation jobs executed as fast as possible when necessary | - Order the execution of computation jobs on others machines - Offer idle time to execute computation jobs of others |
Mitra and Maheswaran (2005) | P | - Agents speedup is as high as possible (speedup is the ratio of time executing jobs on demand when being part of the system divided by not being part of the system) - Computing machines should be continuously connected as long as possible (without interruption) | Have highest execution speed of jobs on demand | - Being connected to the network - Not being connected to the network |
Telecommunication Networks | ||||
Shen et al. (2014) | N | Minimize the network outage probability (outage is when an agent wants to call but cannot) | Have highest call quality when calling | - Turn of connection to small cell (for a certain period of time), be silent, to not disturb others with their signal - Have an active connection to a small cell |
Efstathiou (2006) | P | Network coverage (members can consume WLAN connections when they are mobile and thus experience better internet access) | Have access to as much WLAN as possible when being mobile | - Provide WLAN to other members (when at home) - Consume WLAN from other members (when mobile) |
Buttyán and Hubaux (2001, 2003); Mastronarde et al. (2015); Xu and van der Schaar (2013) | P | The success rate of relay transmissions on demand | Have highest availability of mobile internet when needed (either through a direct connection to mobile internet base station or through relay transmission) | - Request relay transmission from other agents on demand - Offer relay transmissions to other agents on demand |
Road Transportation Networks | ||||
Pedroso et al. (2023); van de Sanden (2022) | N | Optimal mesoscopic flow (can be aggregated perceived discomfort of all commuters, could also include other goals such as minimizing emissions) | Minimize personal discomfort traveling on a specific way at a specific time (discomfort can be seen in terms of speed respectively how many others use the same way) | - Stay at home - Choose one of the many possible ways of travel and pay a price for it defined by central operator (price represents Karma points, can also be negative meaning receiving Karma points) |
Censi et al. (2019); Elokda et al. (2023) | N | Minimize inefficiency (average of all vehicle costs) | Minimize personal costs while traveling (costs mean the level of urgency when the agent needs to let pass and 0 otherwise) | - Pass intersection first - Let other agents pass first |
Elokda et al. (2023, 2022) | N | Minimize inefficiency (average of all vehicle costs) | Minimize personal costs (the travel time in context to the urgency at that day) | - Use the normal lane (travel slow) - Use the priority lane (travel faster, pay fee) |
Social Networks | ||||
Prendergast (2022) | P | - Maximize volume of allocated donations - Guarantee the provision of all foodbanks to a minimum | - Guarantee the provision of goods for the poor - Maximize the volume of donations used (did not expire) | - Win auction (receive a truckload of specific food, pay bid) - Loose auction (receive bids of the winner) - Sell left-over food (that would be broken otherwise) |
Johnson et al. (2014) | P | Maximize the number of available babysitters | Have a baby sitter available when necessary | - Use a babysitting service (pay scrip) - Offer a babysitting service (receive a scrip) |
Kim et al. (2021); Roth et al. (2004); Sönmez et al. (2020) | P | Maximize the number of available donations | Minimize the waiting time (to obtain a patient-compatible organ donation) | - Exchange living-organ donation (to another, compatible patient, receive the right to be on priority list or to obtain compatible organ from other pair in return) |
Gale and Shapley (1962); Hylland and Zeckhauser (1979) | N | Maximize overall satisfaction with assignment (maximize average satisfaction) | Get the highest preferred position | - Stay with position (already have) - Exchange position (with another individual) |
Currency | Interaction | Transaction | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Reference | Parity | Balance limits | Amount control | Initialization | Redistribution | Price control | Price limits | Resource provision | Resource allocation | Counter-party | Peer selection | Decision making | Payment amount | Payment receiver | Karma Gain | Karma Loose |
P2P and Cloud Networks | ||||||||||||||||
Telecommunication Networks | ||||||||||||||||
Garcia and Hoepman (2004); Vishnumurthy (2008); Vishnumurthy et al. (2003) | P | N | S | I | N | A | R | P | P | P | M | F | B | P | P | C |
Oliveira et al. (2011) | P | N | N | I | N | A | R | P | P | N | M | F | O | P | P | C |
Mitra and Maheswaran (2005) | T | N | S,E | N | N | S | R | P | S | N | N | F | N | P | P | U,E |
Telecommunication Networks | ||||||||||||||||
Shen et al. (2014) | B | N | N | I | N | N | B | N | P | P | R | F | F | P | P | C |
Efstathiou (2006) | T | N | N | N | N | N | N | P | P | N | R | F | F | P | P | C |
Buttyán and Hubaux (2001, 2003); Mastronarde et al. (2015); Xu and van der Schaar (2013) | B | N | C | I | N | N | B | P | P | N | R | F | F | P | P | C |
Road Transportation Networks | ||||||||||||||||
Pedroso et al. (2023); van de Sanden (2022) | P | N | S | I | N | S | N | N | A | S | / | F | F | S | C | C |
Censi et al. (2019); Elokda et al. (2023) | P | Y | C | I | N | A | R | N | S | P | R | F | B | P | P | C |
Elokda et al. (2023, 2022) | P | N | C | I | N | A | N | N | S | S | M | F | B | A | P | C |
Social Networks | ||||||||||||||||
Prendergast (2022) | P | N | C | I | N | A | N | P | S | N | M | F | B | W | P | C |
Johnson et al. (2014) | B | N | C | I | N | N | B | P | P | N | M | F | F | P | P | C |
Kim et al. (2021); Roth et al. (2004); Sönmez et al. (2020) | B | N | N | N | N | N | B | P | P | P | M | F | F | P | P | C |
Gale and Shapley (1962); Hylland and Zeckhauser (1979) | B | N | C | I | N | N | B | N | P | P | R | F | F | P | P | C |
Literature Field | Application Field |
---|---|
Blockchain | |
Reputation/credibility system | |
Trust system | |
Secure accounting system | |
Micropayment system | |
Trading system | |
Information dissemination technology | |
Scrip system | |
Token economy | |
Credit scheme | |
Crypto currency | |
Lightweight currency | |
Technological prerequisite for… | |
smart contracts | |
distributed hash tables | |
distributed ledger | |
minting proof-of-work | |
Network and Technology | |
Network Protocol | |
Self-Coordination in peer-to-peer computer networks | |
Solution malicious behavior in peer-to-peer networks… | |
free-riding problem | |
hidden actions problem | |
lotus eater attack | |
Sybil attack | |
Eclipse attack | |
spoofing attack | |
Filesharing | |
Computation Resource Sharing | |
File Sharing | |
Economics | |
Self-contained economy | |
Non-monetary market | |
Game Theory | |
Dynamic population game | |
behavior | |
Incentive mechanism | |
Fairness enforcement | |
Altruism enforcement | |
Contribution enforcement |
Appendix B. Karma and Alternatives
1 | The database queries were conducted on 1 October 2023. |
2 | Python package tomotopy (v.0.9.0, https://bab2min.github.io/tomotopy/v0.9.0/en/). Accessed on 1 October 2023. |
3 | https://medium.com/blockwhat/03-it-s-karma-484fdc2d8657. Accessed on 1 October 2023. |
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Design Parameter | Option |
---|---|
Currency | |
Parity | Price, Threshold, Binary |
Balance limits | Unlimited, Bounded (upper, lower) |
Amount control | Constant (per capita), Uncontrolled, Expiry, System |
Initialization | Equal endowment, Weighted endowment, Random endowment, None |
Redistribution | Property tax, Payment tax, Lottery, None |
Interaction | |
Price control | Auction, Centrally defined, None |
Price limits | Only positive, Binary, None |
Resource provision | By agent(s), By system |
Resource allocation | Auction winner, System decision, Provider decision |
Counter-party | N agents, One agent, System |
Peer selection | Market, neighborhood, Randomly assigned, Active selection |
Decision-making | Free |
Urgency process | Homogeneous, Heterogeneous |
Temporal preference | Homogeneous, Heterogeneous |
Transaction | |
Payment amount | Bid, Peer’s bid, Difference in bids, System Order, Fixed, Nothing |
Payment receiver | Resource provider, System, Equally across population, Weighted across population |
Karma gain | Resource provision, Resource consumption |
Karma loose | Resource consumption, Expiration, Rule-violation |
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Riehl, K.; Kouvelas, A.; Makridis, M.A. Resource Allocation with Karma Mechanisms—A Review. Economies 2024, 12, 211. https://doi.org/10.3390/economies12080211
Riehl K, Kouvelas A, Makridis MA. Resource Allocation with Karma Mechanisms—A Review. Economies. 2024; 12(8):211. https://doi.org/10.3390/economies12080211
Chicago/Turabian StyleRiehl, Kevin, Anastasios Kouvelas, and Michail A. Makridis. 2024. "Resource Allocation with Karma Mechanisms—A Review" Economies 12, no. 8: 211. https://doi.org/10.3390/economies12080211