jaggpy 0.1

A Python package for Judgment Aggregation

JAGGPY

jaggpy is a Python package for computing outcomes of judgment aggregation (JA) scenarios. The package allows for JA scenarios in the most general form, where issues in the agenda, constraints on judgment sets and constraints on the outcome may be arbitrary formulas. For more information on the formal framework used see, e.g.,

Endriss, U., de Haan, R., Lang, J., & Slavkovik, M. (2020). The Complexity Landscape of Outcome Determination in Judgment Aggregation. Journal of Artificial Intelligence Research, 69, 687-731.

in which it is referred to as framework (6).

The package offers two ways in which to generate outcomes for a scenario given a JA rule. The first (and slowest) is a brute force solver, and the second makes use of Answer Set Programming (ASP) for the efficient computation of outcomes, building on the encodings presented by de Haan and Slavkovik in

de Haan, R., & Slavkovik, M. (2019). Answer set programming for judgment aggregation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019). AAAI Press.

Table of contents

1. Getting started
   • Prerequisites
   • Installation
2. Usage
   • Scenario objects
     • Formulas
     • .jagg file
     • Creating a Scenario Object
     • Scenario methods and properties
   • Using the solver
     • Creating a solver object
     • Solver methods
   • Examples
3. License
4. Acknowledgements
5. Contact
6. References

Getting Started

Prerequisites

At least Python 3.4 is required.

Installation using PIP

To install the package run the following command

pip install jaggpy

If necessary, this also installs dependencies of the package. These include:

• clingo
• nnf
• pyparsing

Usage

To use this package you need to create a scenario object for the scenario that you want to apply judgment aggregation to. Futhermore, a solver object needs to be made to apply one of the aggregation rules to the scenario. These objects and their methods will be discussed in what follows.

Scenario objects

Formulas

This package can handle arbitrary formulas in the agenda, input-, and output constraints. The available operators are conjunction (&), disjunction (|), negation (~) and implication (->). Parentheses can usually be omitted where clear from context. A few example formulas are:

 (~x1 | ~x2 | ~x3) & (~x1 | ~x3 | ~x4)

 (x1 -> ~(x2 & x3)) & (x1 -> ~(x3 & x4)))

 ( ~x1 & x2 ) -> x3

.jagg file

An object from the scenario class is read from a .jagg file. A .jagg file should be formatted as follows:

• var_1,...,var_n: A list of all the variables occuring in the scenario.
• m: The number of issues in the pre-agenda. (Note that only a pre-agenda needs to be specified. The agenda will be automatically generated. A pre-agenda contains all the issues in their unnegated form. The agenda additionally includes their negations.)
• X, Formula: For each of the m formulas in the pre-agenda, the number X as a label followed by the formula.
• In, Formula: The input constraint labeled by the string "In". (Note that it is possible to have be multiple input constraints.)
• Out, Formula: The output constraint labeled by the string "Out". (Note that it is possible to have be multiple output constraints.)
• v, j: The number of voters v followed by the number of distinct judgment sets j.
• J, Label_1;...;Label_n: The number of times this judgment set occurs followed by the labels of the accepted formulas. The formulas should be separated by a semicolon. Issues that are not accepted are rejected.

By starting a line with a "#" the line will be ignored in the reading of the file. Blank lines will also be ignored. Using these comments and blank lines the .jagg file can be made more readable.

An example of the format of a .jagg file is:

x1, x2, x3
4

# The issues
1, x1
2, x2
3, x3
4, ( ~x1 & x2 ) -> x3

# The constraints are the same
In, ( x1 | ~x1 )
Out, ( x1 | ~x1 )

# We have 8 voters and 3 different judgment sets
8, 3
3, 2;3;4
2, 1;2;4
3, 4

In this scenario we have the variables x1, x2 and x3. There are 4 different issues in the agenda. These issues are x1, x2, x3 and ( ~x1 & x2 ) -> x3, and are labeled by the numbers 1-4 respectively. The input and output constraints are ( x1 | ~x1 ). There are 8 voters and 3 different judgment sets. Of the voters three have accepted the issues labeled by 2, 3 and 4 (and thus have rejected the issue labeled by 1). Two have accepted the issues labeled by 1, 2 and 4 (and thus have rejected the issue labeled by 3). And three have accepted the issue labeled by 4 (and thus have rejected the issues labeled by 1, 2 and 3).

Creating a Scenario Object

A scenario object should first be created, and then can be loaded from a .jagg file given its path. The path should be given as a string, i.e., of the form "path/to/file/". It can then be loaded using the load_from_file method of the scenario class. For example:

from jaggpy.classes import Scenario

scenario1 = Scenario()
PATH = "path/to/file"
scenario1.load_from_file(PATH)

If there are any inconsistencies in the scenario defined in the .jagg file, a warning indicating the inconsistency will occur. It is not possible to successfully load an inconsistent scenario.

Scenario methods and properties

A scenario object has several useful methods and properties.

• pretty_repr(): A method that returns a string that represents the scenario in a readable format.
• agenda: This property is a dictionary with the the labels of issues as keys and the issues as values.
• input_constraints: This property is a list of the input constraints.
• output_constraints: This property is a list of the output constraints.
• profile: This property is a list of the judgment sets. Each element is a list of which the first element is the number of times the judgment set occurs. The second element of these lists is a list with the issues that are accepted.
• number_voters: This property is an integer specifying the number of voters.

Using the solver

Creating a solver object

In order to call a solver, we first need to create a solver object. We can create a brute force solver object and an ASP solver object as follows:

from jaggpy.bf_solver import BFSolver
from jaggpy.asp_solver import ASPSolver
bfs = BFSolver()
asp = ASPSolver()

Solver methods

There are a number of different aggregation rules that are supported. For the brute force solver these are:

• kemeny
• maxhamming
• slater

For the ASP solver these are:

• kemeny
• slater
• leximax
• young
• majority

There are three solver methods corresponding to different ways in which the output is given. For each of these the first argument is the the scenario object and the second argument a lowercase string specifying the rule to be executed. Also, each method has a verbose property that, if turned on, prints the used rule and solver. This property is false by default.

The methods are the following:

• solve() This method uses no additional arguments. The output given is a generator. Note that the generator returned by the brute force solver yields a list of all outcomes while the ASP solver's generator yields individual outcomes.
• enumerate_outcomes() This method uses no additional arguments and prints all the outcomes.
• enumerate_first_n_outcomes() This method takes one additional argument n and prints the first n outcomes.

For example, we can compute and enumerate outcomes generated by the Kemeny rule, given the scenario object scenario1 as follows:

asp.enumerate_outcomes(scenario1, "kemeny")
asp.enumerate_outcomes(scenario1, "kemeny", True)
bfs.enumerate_first_n_outcomes(scenario1, "kemeny", False, n=2)

Examples

The source code of this package contains the folder src/test. There are several example files illustrating the use of the jaggpy package. It contains several example scenarios, an example python file incorporating the package and explicit ASP encodings of some scenarios and rules.

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgements

The encodings in this package are created by Bo Flachs, Philemon Huising and Ronald de Haan

Contact

Ronald de Haan - [e-mail]

Github Link: https://github.com/rdehaan/jaggpy

References

• Endriss, U., de Haan, R., Lang, J., & Slavkovik, M. (2020). The Complexity Landscape of Outcome Determination in Judgment Aggregation. Journal of Artificial Intelligence Research, 69, 687-731.
• de Haan, R., & Slavkovik, M. (2019). Answer set programming for judgment aggregation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019). AAAI Press.