Robust Incentive Mechanisms �for Blockchain and AI Systems
Talk for TAU IISE
January 2026
Yotam Gafni
Weizmann Institute of Science
Change
1
We Can Believe In?
2
“A more serious issue is collusion between the proposer and some transaction senders.“
[Vitalik Buterin, “Blockchain Resource Pricing” 2018]
“Frequent exchanges of information that facilitate a better common understanding of the market and monitoring of deviations increase the risks of a collusive outcome.“
[European Commission Guidelines to Horizontal Cooperation Agreements 2023]
Change
New�Technology
3
🡪
New�Assumptions
New�Mechanisms
🡪
4
Security
Align Incentives
with System Goals
Fairness
Make the System Work
for Everyone
Optimality
Tune the System
to Maximize Objectives
Robustness to Collusion
Safety of Data-Sharing Protocols
Weak Identity & Recourse
Consumer Effects of Learning
Fair Exploration & Allocation
Centralization Dynamics
Minority Rights
Prizes in Data Science Contests
Outsourcing
Priority under Time-Sensitivity
5
Security
Align Incentives
with System Goals
Fairness
Make the System Work
for Everyone
Optimality
Tune the System
for Max Performance
Robustness to Collusion
[GY EC’24 Revision@GEB,
FGR TLDR’24 🏭,
GY MARBLE’24, G’25]
Safety of Data-Sharing Protocols
[GT EC’22]
Weak Identity & Recourse
[GLT AAAI’20, GT TARK’23]
[GLT IJCAI’21, JAIR’22]
Prizes in Data Science Contests
[DGLLL AAAI’23, GEB’25]
Outsourcing
[FG’26]
Priority under Time-Sensitivity
[GY’22]
Consumer Effects of Learning
[GGT SAGT’24, Revision@TEAC]
Fair Exploration & Allocation
[BP-GM’25, GHLT-C TEAC’23]
Centralization Dynamics
[G’25]
Minority Rights
[GG’24 🏆]
Blockchains are Permissionless
6
🡪
No Trust in Miners
Collusion-Robust Mechanisms
🡪
7
Mechanisms for Data Workflows
Mechanisms for Blockchain Transactions
Mechanisms for Decentralized Governance
Blockchain Transaction
Fees
Robustness to Collusion [Gafni & Yaish EC’24, Revision@GEB]
Security
Align Incentives
with System Goals
Blockchains: A Soft Intro
8
Total Blockchain Market Cap
Blockchain Market Cap Partition
$6T
$4T
$2T
$0
‘14 ’15 ‘16 ’17 ‘18 ‘19 ‘20 ‘21 ‘22 ‘23 ‘24 ‘25
‘14 ’15 ‘16 ’17 ‘18 ‘19 ‘20 ‘21 ‘22 ‘23 ‘24 ‘25
100%
75%
50%
25%
0%
Bitcoin
Ethereum
Stablecoins
Other
Blockchains: A Soft Intro
9
Blockchains: Why?
Why not traditional transaction systems?
10
“Competition among service providers within the platform and free entry imply no entity can profitably affect the level of fees paid by users.”
Huberman, Leshno & Moallemi, REStud ‘21
“Competition among service providers within the platform and free entry imply no entity can profitably affect the level of fees paid by users.”
Huberman, Leshno & Moallemi, REStud ‘21
But it comes with its own set of challenges…
Cost to Send USD Internationally
$44 via International Wire Transfer
$12 via USDC on Ethereum, ’21 avg
$1 via USDC on Ethereum, Sep ’24 avg
<$0.01 via USDC on Base L2,Sep’24 avg
[a16z crypto, State of Crypto 2024 Report]
Blockchains: Technical Primer
Users
Miners
Block
Block
Transaction
Blockchain
Block
Block
A Random Miner is Given Temporary Monopoly Power
Block
Reward
Fees
To prevent Sybil Attacks, the random choice depends on a finite resource
In Bitcoin, transactions allow payments. In Ethereum, they are Turing-Complete and can encode any logic.
Transaction Fees
12
Users
Block
Miner
TX
TX
TX
Max size:
2 TXs
Max Eth
Block
Source: mempool.jhoenicke.de
Pending TXs
Date
Bitcoin’s Transaction Fee Mechanism
13
EIP-1559: Ethereum’s New TFM
Goal 3: Understand whether burning matters
14
Tip
Base
Tip
Base
Ethereum fees before and after EIP-1559 [LLNZZZ CCS’22]
TX
New Block of Size 2
Base Fee
A total of >4m ETH were burned since EIP-1559
Transaction Fee Mechanisms (TFMs)�
15
The foundational open problem of TFMs:
Existence of a simple for users, miner non-manipulable, and robust to collusion TFM?
16
No deterministic mechanism satisfies all desiderata.
A gap in welfare exists for randomized mechanisms.
[Gafni & Yaish EC’24, Minor revision at Games and Economic Behavior]
1st and 2nd price auctions
17
1st-price auction
2nd-price auction
Pays its own bid
We focus on single-item auctions in the talk.
The paper characterizes the multi-item setting.
Highest bidder wins
Pays second highest bid
Highest bidder wins
Collusion in Auctions
“The most important features of an auction are its robustness against collusion and its attractiveness to potential bidders. Failure to attend to these issues can lead to disaster.“
[Paul Klemperer, “What Really Matters in Auction Design” 2002]
”The FCC and others conducting similar auctions should think carefully about the tradeoff between more informed price discovery and the risk of collusive bidding.”
[Cramton & Schwarz,
“Collusive Bidding: Lessons from the FCC Spectrum Auctions” 2000]
All the more relevant for Blockchains’ anonymous environments!
18
Simple for Users ✅
Robust to Miner manipulation ✅
Pays set price (1.5)
Ok, bidder 1, just say you’re willing to pay 1.5, and I’ll cash you back 1
1.5
1
The true value for bidder 1
Bidder 1’s payment
Miner’s transfer
Arbitrary winner above a set price
Problem?
Hint: Bad price discovery, which motivates collusion
Some Notations…
20
The Desiderata
21
22
”Myerson’s Lemma” [Myerson ‘81] :
UIC <=> monotone allocation, payment uniquely determined by allocation.
23
No revenue…
What if we only have Global-SCP?
24
Less burn, same value.
Bidder and Miner can balance using transfers.
What if we only have Global-SCP?
do not allocate (individual rationality constraint)
25
Adding UIC and MIC into the mix…
Our characterization of Global-SCP:
Constant burn, highest-bidder allocated if and only if higher than the burn.
26
Achieving all together is impossible.
With UIC:
Second-price auctions with a reserve that is burned.
With MIC:
“Generalized first-price” auctions
A slightly generalized EIP-1559/Bitcoin Mechanism!
Randomized Mechanisms
So far, we discussed only deterministic mechanisms.
We always assumed a specific bidder is allocated.
What if we allow a random choice of who is allocated?
27
Randomized Scale-invariant Mechanisms
28
Randomized Scale-invariant Mechanisms
29
Randomized Scale-invariant Mechanisms
30
General Randomized Mechanisms
Proof-sketch: We have 0 revenue with a single bidder (burn=payment).
Without (P1), miner would create a shill bidder.
31
General Randomized Mechanisms
32
Takeaways
33
34
Mechanisms for Data Workflows
Mechanisms for Blockchain Transactions
Mechanisms for Decentralized Governance
Data-Sharing Protocols
Exclusivity Attacks [Gafni & Tennenholtz EC’22]
Security
Align Incentives
with System Goals
Emergence of Data-Sharing Protocols
35
🡪
Exclusivity Attacks
🡪
Robust Protocols
�A growing interest in building shared models…
36
Building Shared Models
37
Federated Learning: THIS SHOULD BE A SLIDE, WHERE I SHOW HOW FL WORKS, WITH ANIMATIONS, AND THEN A SLIDE ABOUT FREE RIDING
38
Known Issue: Free-Riding
39
Coordinator Server
Client 1
Client 2
Client 3
Local Data 1
Local Data 2
Local Data 3
Model
Federated Learning
40
Coordinator Server
Client 1
Client 2
Client 3
Local Data 1
Local Data 2
Local Data 3
Model
Model
Model
Federated Learning
41
Coordinator Server
Client 1
Client 2
Client 3
Local Data 1
Local Data 2
Local Data 3
Local Gradients 1
Local Gradients 2
Local Gradients 3
+
+
3
Updated Model
Federated Learning
42
Coordinator Server
Client 1
Client 2
Client 3
Local Data 1
Local Data 2
Local Data 3
Model
Free-Riding is a threat!
43
Coordinator Server
Client 1
Client 2
Client 3
Local Data 1
Local Data 2
Local Data 3
Model
Model
Model
Free-Riding is a threat!
44
Coordinator Server
Client 1
Client 2
Client 3
Local Data 1
Local Data 2
Local Data 3
Local Gradients 1
Local Gradients 2
Local Gradients 3
+
2
Updated Model
Free-Riding is a threat!
45
Client 1
Client 2
Client 3
Updated Model
Updated Model
Coordinator Server
Local Data 1
Local Data 2
Local Data 3
Updated Model
Local Gradients 3
Free-Riding is a threat!
46
“For Federated Learning, incentive mechanism design for honest participation is an important practical research question […] particularly relevant in the cross-silo setting, where participants may at the same time be business competitors.“
[Kairouz et al., “Advances and Open Problems in Federated Learning” 2021]
A General Concept:�Exclusivity Attacks
where all other agents report truthfully and accept the model,
can an attacker launch a successful exclusivity attack?
47
Example: The one-shot function SUM
48
20
10
+
=
30
Example: The one-shot function SUM
49
30
30
A successful attack on one-shot SUM
50
20
10
+
=
A successful attack on one-shot SUM
51
30
A failed attack on one-shot MAX
52
20
10
,
)=
max(
A failed attack on one-shot MAX
53
??
54
We consider a long-term interaction
Continuous Protocol:
Conditionally Vulnerable
Universally vulnerable
Linear Regression in d features
(d-LR)
k-Center Clustering
Yes
Yes
No
Periodic Protocol: d-LR, k-Center are not vulnerable.
Formal model – Continuous Protocol
55
A failed attack on continuous MAX
56
Ledger update:
120
Ledger update:
90
Nature
Agent 1
Agent 2
Ledger
True update:
90
User update:
90
Ledger update:
90
User update:
120
Ledger update:
120
True update:
90<X<120
User update:
90<X<120
Ledger update:
120
Ledger update:
120
Observed history, Strategy and Vulnerability
57
Ledger update:
120
Ledger update:
90
Nature
Agent 1
Agent 2
Ledger
True update:
90
User update:
90
Ledger update:
90
User update:
120
Ledger update:
120
True update:
90<X<120
User update:
90<X<120
Ledger update:
120
Ledger update:
120
Observed History: All the messages agent j sees.
Update Strategy: Mapping from observed histories to user updates.
Truthful: a user updates U iff the user received true update U
Observed history, Strategy and Vulnerability
58
Ledger update:
120
Ledger update:
90
Nature
Agent 1
Agent 2
Ledger
True update:
90
User update:
90
Ledger update:
90
User update:
120
Ledger update:
120
True update:
90<X<120
User update:
90<X<120
Ledger update:
120
Ledger update:
120
Observed history, Strategy and Vulnerability
59
Ledger update:
120
Ledger update:
90
Nature
Agent 1
Agent 2
Ledger
True update:
90
User update:
90
Ledger update:
90
User update:
120
Ledger update:
120
True update:
90<X<120
User update:
90<X<120
Ledger update:
120
Ledger update:
120
Linear regression
60
Challenge for the Attacker:
How to ‘reverse’ effects of fake points submitted?
A temporary omission conditional attack
61
A Universal Attack
62
A Universal Attack
63
Example of a universal attack on �Linear Regression with one feature.
64
Truthful
Universal Attack
Example of a universal attack on �Linear Regression with two features.
65
Takeaways
66
Future Directions
67
Future research preview: �What happens when we introduce noise?��Idea: Return the LR estimator with small additive noise
68
Concurrent Work on Incentives in Learning
69
Concurrent Work on Incentives in Learning
70
where many competitions compete for user attention?
Looking Forward…
71
72
UNISWAP Foundation Fellowship!
Robust TFM Design
Blockchains Beyond TFMs
73
Incentives & Economics of AI
Fair Allocation: Theory to Practice
Thanks for listening!�
Email: yotam.gafni@gmail.com
Website: https://www.yotamgafni.com
Follow-up and Ongoing Work
75
Follow-up and Ongoing Work
76
Follow-up and Ongoing Work
77
Uniswap Foundation Fellowship!
Follow-up and Ongoing Work
78