Optimizing Trading Assignments in Water Right Markets
Authors: Yicheng Liu, Pingzhong Tang, Tingting Xu, Hang Zheng
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both algorithms are tested on data sets generated from real historical trading data. Our study also suggests the importance of reducing transaction costs, for example, institutional costs in water market design. Our work opens a potentially important avenue of market design within the agenda of computational sustainability. Experimental evaluation In this section, we develop a data generator based on historical data. Then, we characterize a model to simulate the current assignment algorithm as our baseline. Finally, we compare the efficiency and scalability among our MIP algorithm, our modified LP algorithm and the currently utilized algorithm. |
| Researcher Affiliation | Academia | Yicheng Liu and Pingzhong Tang Institute of Interdisciplinary Information Sciences, Tsinghua University, Beijing, China Tingting Xu and Hang Zheng State Key Lab of Hydro-science and Engineering Tsinghua University, Beijing, China |
| Pseudocode | Yes | Algorithm 1 Dynamic programming algorithm for MAXWELFARE on a line graph Input: Given a line graph G = (V, E), vi, price pi, demand/supply qi. vivj E, transaction threshold thij. Output: maximum social welfare and the corresponding flow assignments. 1: In this algorithm, We call Optimization 2 via solve(input(i, j)), where input(i, j) denotes all the inputs on the line interval between vi and vj. 2: ans( 1) 0, ans(0) 0, ans(i) denotes the maximum social welfare on the subgraph between v1 and vi. 3: F( 1) {}, F(0) {}, F(i) denotes the trading volume assignment corresponding ans(i) 4: for i 1, 2, . . . , n do 5: ans(i) 0, F(i) {} 6: for j 0, 1, . . . , i do 7: tempans, temp F solve(input(j, i)) 8: if ans(i) < tempans + ans(j 1) then 9: ans(i) tempans + ans(j 1) 10: F(i) F(j 1) temp F 11: end if 12: end for 13: end for 14: Output ans(n) as maximum social welfare, F(n) as the corresponding assignments. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions generating data based on historical trading data: "We develop a data generator based on historical data... We make use of the fact that about 80 percent of all the trades are between two villages within 10 kilometers... We use the actual locations of villages in the real-world map of Xiying irrigated area... Each time, one agent appears at the position of one village in Xiying irrigated area and its supply or demand is generated according to the historical distribution." However, no specific public dataset is named, linked, or cited that provides the historical data or the generated data for public access. |
| Dataset Splits | No | The paper mentions using "data sets generated from real historical trading data" for testing algorithms, but it does not specify any explicit training, validation, or test dataset splits or cross-validation setup. |
| Hardware Specification | Yes | All our experiments are conducted on a desktop with a quad-core CPU and 4G RAM. |
| Software Dependencies | Yes | We will show in the experiment section that, with MIP solvers such as CPLEX 6, we can scale this problem to considerably large instances. |
| Experiment Setup | Yes | The thresholds are set to be one third of one s supply or demand. The valuations (asks and bids) on each unit of water are uniformly distributed between 0.15 and 0.25 yuan per m3. |