The 20th summer course of behavior modelling in transportation networks
Yusuke Hara (Tohoku University) What is mechanism design??
• A field in economics and game theory • Hurwicz, Maskin and Myerson received Nobel economics prize in 2007.
• What type of problems?
• Auction theory • Mirrlees and Vickrey received Nobel economic prize in 1996. • Matching theory • Roth and Shapley received Nobel economic prize in 2012. • Public goods supply problem
•Developing mechanisms for allocating limited resources efficiently. • Applied field of game theory
• Game Theory
• Under a game setting, we analyze the Nash equilibrium.
• Mechanism Design
• To implement an optimal equilibrium (target), we make a rule of game in the game theory. • Key concepts • Efficiency (Maximizing the social welfare) • Strategy-proofness (Avoiding the strategic behavior)
route A route B • Wardrop's first principle • It is the concept of equiribrium. • No driver can unilaterally reduce his/her travel costs by shifting to another route. • This is equivalent to Nash equilibrium in game theory.
Non busy traveller Busy traveller •How should we solve this problem?
if you are in a hurry, you can use route A. Otherwise, you use the other route B. 7
• Are you an honest person?
•How should we decide the price? • In general, it is difficult to know each traveler’s willingness to pay (WTP).
•Akamatsu (2007) proposed tradable bottleneck permit system. •Every driver bids a permit to use road network (link level or path level). • It means they state the information about their value of time (VOT) and desired arrival time. • After bidding, the policy maker determine the allocation through a mechanism. In general, VCG auction mechanism is used. • This mechanism has a good characteristics. • Efficiency: The final result is the most efficient allocation. • Strategy-proofness: This mechanism eliminates drivers’ lie.
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• VCG mechanism satisfies efficiency and strategy-proofness (truth telling). •Mechanism design approach can work well. • But, •Do you want to bid every time slots of every transportation modes (train, bus, road network, etc.) every day????
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Behavioral mechanism design for transportation services: Laboratory experiments and preference elicitation cost Yusuke Hara, Transportation Research Part B: Methodological, Vol.115, pp.231-245, 2018.
• Transportation system such as road network and public transport have a supply capacity. • For the efficient transport system, it is important to allocate the resource efficiently. • One Approach: Tradable permit system/Auction • Tradable permit: Akamatsu (2007), Hara and Hato (2018) • Tradable credit: Yang and Wang, Wu et al., Nie and Yin • Auction mechanism such as VCG mechanism can achieve the strategy-proofness and efficient allocation, but in reality, there is cognitive cost for preference elicitation. • Hara and Hato (2019) shows there exists the cognitive cost for transportation auction system in bicycle sharing auction empirically. • Users don’t behave rationally in reality.
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The viewpoint of Traditional Mechanism Design Constraint Conditions of Transportation System Transport/Activity Demand (Valuations) represented by users Efficient Allocation for Social Good
Partially represented True/Latent Transport/Activity Demand (Valuations)
¦ Bounded Rationality Transaction cost Cognitive cost Cognitive bias Misunderstanding etc The Viewpoint of this study
• The research questions of this study Are there cognitive costs of preference representation in reality? 2. If they do exist, is it possible to design preference elicitation mechanisms that can reduce such cognitive cost for efficient allocation? • The contributions of this study 1. We designed and executed experiments to analyze differences in elicitation mechanısms. 2. Based on the experiment, we empirically showed that valuations and the number of representations vary depending on a preference representation system. 3. he differences in the number of representations resulted in a thin market. A thin market means a market with a low number of actions. As a result, the differences in the number of representations reduce the efficiency of the auction results. 4. We showed that we can significantly improve the efficiency of auction via increasing the number of preference representations using the preterence-prediction mechanism.

• The objective of our experiment
1. The differences of elicitation mechanisms have effect on the quality of participants’ representation such as the number of representation which the subject represents the positive WTP and the value of WTP. 2. As a result, the quality of participants’ representations affects the allocation result and efficiency of auctions.
The overview of this experiment
• We collected 1647 participants who were 20-59 years old in the Tokyo metropolitan area. • Our experimental design is Randomized Controlled Trial (RCT). And we assigned participants to three groups (n = 550, n = 547, and n = 550) randomly. • Each participant represents the Willingness to Pay (WTP) for transportation service in three situations.
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• Situation 1: The reserved seat for rush hour commuter train in Tokyo metropolitan area • WTPs for reserved seat of rush hour commuter train. • The frequency of the train is every 15 minutes. • The time you start working is 9am. • Situation 2: The flight from Tokyo to Fukuoka on business trib • WTPs for the flight from Tokyo to Fukuoka. • The frequency of flights is every 30 minutes. • The meeting begins at 1pm. • Your company pays all the travel fee. • Situation 3: The flight from Tokyo to Okinawa for leisure trip • WTPs for the flight from Tokyo to Okinawa. • The frequency of flights is ever 1 hour. • You need to pay for your own round-trip flight.
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1.
(a) step-by-step representation Choice and representation of WTP Many page transitions occur.
2. all-in-one representation
Service Permit 1 300 Yen The WTP of All usage permits are presented in the table. But you need to fill in the valuations for all usage permits. (The default value is 0.)
If you represent a valuation, the system predict the valuations of other time slot. 20 And you can modify the valuations which are predicted by the system.
●  ●  ●  ●  ●  ●  ●  . 風歴ブックマークユーザー ウインドウヘルプ✆ harapon.sakura.ne.jp/wtp_survey_poiuy/survey01.phpシチュエーション1:満員の通勤鉄道で必ず着席できる
権利(着席券)
設定
•乗車予定の鉄道は下表に示すように、15分おきに運行されています。 •あなたの家の最寄り駅から会社の最寄り駅までの乗車時間は45分間で乗り換

選択肢
以下の各時間帯の着席券の中から、あなたにとって最も望ましい（最もお金を支払ってもよい）着席券を選択してください。
☆ O : 21
ファイル  類集  表示  履歴  ブックマーク  ユーザー   ウインドウ   ヘルプ●  ●  ●  ●  ●  ●  ●  ● baranon sakura ne in/wtn sur ① harapon.sakura.ne.jp/wtp_survey_lkjhg/survey01.php ☆ O :シチュエーション1:満員の通勤鉄道で必ず着席できる
権利(着席券)
設定

選択肢
以下の各時間帯の着席券に関して、あなたがそれぞれ支払ってもよいと思える最大の金額(支払い意思額)を入力してください。不要な着席券は0円としてください。
関節  ブックマーク ユーザー　ウインドウ　ヘルプ93% ■■　A　9月9日(土) 17:54     Q ● ●せ: o ☆ O : ✆ harapon.sakura.ne.jp/wtp_survey_mnbvc/survey01_1.phpシチュエーション1:満員の通勤鉄道で必ず着席できる
権利(着席券)
設定
•あなたの自宅から自宅の最寄り駅までは徒歩10分です。 •会社の最寄り駅からあなたの会社までは徒歩5分です。 •乗車予定の鉄道は下表に示すように、15分おきに運行されています。 •あなたの家の最寄り駅から会社の最寄り駅までの乗車時間は45分間で乗り換えはありません。 •鉄道の乗車料金は片道360円です。 •非常に混雑する鉄道のため、着席券なしでは座ることができる可能性は低いです。2週間に1回程度の頻度で偶然座ることが可能です。
選択肢
以下の各時間帯の着席券の中から、あなたにとって最も望ましい（最もお金を支払ってもよい）着席券を選択してください。
The arrival time of the most highest WTP is 8:30am in all elicitation mechanism.
The WTP of all-in-one elicitation is higher than other mechanisms. 25
The desired departure time is 7am or 8am. The WTP of all-in-one elicitation is higher than other mechanism. 26
step-by-step: 2-3, all-in-one: 4-5, preference-prediction: 6-7 →Under the same situation and RCT, the preference elicitation mechanism 27 has the impact on the number of representations.
• The preference elicitation mechanism has the impact on the number of representation. •  A naïve elicitation mechanism results in thin market. • The formulation of tradable permit auction subject to
single unit demand capacity limit ∀t ∈ T ∀i ∈ I, ∀t ∈ T : Valuation vector of user i : Allocation variable of user i at time slot t • From the participants set, we resampled 200 participants 1000 times. • The capacity limit at all time slot is 100.
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• The distribution of social welfare of each elicitation mechanism • The number of resampling: 1000 e sm. nism JOSE
6. Conclusion
• The contributions of this study 1. Design the experiments to analyze differences in elicitation mechanisms. 2. Based on the experiment, we empirically showed that valuations and the number of representations vary depending on a preference elicitation mechanism. 3. The differences in the number of representations resulted in a thin market. As a result, the differences in the number of representations reduce the efficiency of the auction results. 4. We showed that we can significantly improve the efficiency of auction via increasing the number of preference representations using the preference-prediction mechanism.
• For future work
• In repeated situations, we can improve the preference-prediction mechanism using historical data of each user. • If there are similar characteristics between items such as OD and timeslot, we may use the approach of collaborate filtering.
Thank you!