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Veto bargaining simulator - The 90th Congress

This page allows you to replicate and modify the simulations discussed in my paper, "The Item Veto's Sting." Be warned that these simulations may not make much sense if you have not read the actual paper. For an explanation of this table, scroll down.

In this version of my simulations, I use common space DW-NOMINATE scores from the 90th U.S. Congress (1967-1968). I have rescaled the scores to range from -100 to +100 instead of -1 to +1. For the main simulator, go here.

Simulation results — 500 trials in each dimension

Stalemate Legislature Gets its Ideal Point Compromise Overall Item veto's average utility effect How often does the item veto matter?
Dims. Trials Freq. Freq.Gov's utilityLeg's utility Freq.Gov's utilityLeg's utility Gov's mean utilityLeg's mean utility Gov's mean gainLeg's mean gain Freq. helpsMean gainFreq. same
1 500 31.8% 52.0% 29.2 36.8 16.2% 0.0 29.7 15.2 24.0 0.0 0.0% 0.0 0.0% 0.0% 0.0 100%
2 500 0.0% 54.2% 43.7 78.0 45.8% 0.0 31.6 23.7 56.7 7.2 30.6% −11.6 −20.4% 49.0% 14.8 50%

Explanation of table column headings:

  • Dims: Number of dimensions for this set of simulations. How many issues are addressed simultaneously in each bill?
  • Trials: How many simulations were run for this number of dimensions? I used a very high number of trials in my paper, but I have lowered this value substantially for this web interface to keep the page reasonably quick.
  • Stalemate: As explained in the paper, each trial can have one of three outcomes. "Stalemate" means no compromise was possible. Anything the legislature proposed would have met a veto. Neither player experiences a change in utility, since the status quo does not move.
    • Freq. shows how frequently stalemates occurred.
  • Legislature gets its ideal point: Another of the three possible outcomes. This means that the legislature proposed its exact ideal point as the status quo, and the governor did not veto this proposal.
    • Freq. shows how frequently this outcome occurred.
    • Gov's utility gives the governor's average utility gain under this outcome. (The governor always gains utility, or else she would have vetoed the proposal and kept the status quo.)
    • Leg's utility gives the legislature's average utility gain under this outcome.
  • Compromise: The other possible outcome. The legislature proposed a new status quo that was marginally closer to the governor's ideal point than the old status quo. (You can set the size of this margin below.)
    • Freq. shows how frequently this outcome occurred.
    • Gov's utility gives the governor's average utility gain under this outcome. (The legislature will never allow the governor more utility than necessary, so this will always equal the "acceptance margin" setting specified below.)
    • Leg's utility gives the legislature's average utility gain under this outcome.
  • Overall gives each player's average utility gain, averaging across all three types of outcome. Be careful when comparing these values across dimensions, as the certain types of outcome become more common as dimensionality increases, which has an effect on these averages.
  • Item veto's average utility effect: In (e.g.) two dimensions, it's possible to calculate what the new status quo would have been if the two issues bundled into a single bill had been addressed separately (or with an item veto). In each iteration, I calculate what the utility gain for each player would have been if they had moved the status quo to this point; then, I subtract it from their utility gains under multidimensionality. You can read more about how these utility gains are calculated here.
    • Gov's mean gain shows the governor's average "gain" from having an item veto. My paper explains why this is positive. Governors fare better when issues are handled separately than when the legislature bundles them, and an item veto can separate bundled issues. There are two columns here. The first shows the governor's "gain" in utils; this value is contingent on the "standard deviation" setting below. To escape this contingency, the second column gives the governor's gain as a a percent of the utility he would have gained under multidimensionality.
    • Leg's mean gain is the same idea. It's negative for the same reason that the governor's gain is positive.
  • How often does the item veto matter? This column expands on the info available in the "item veto's average utility effect" columns.
    • Freq. helps shows how often the governor would get a better utility outcome with an item veto than without one. Put differently, it tells you how often the governor does worse under multidimensional bargaining over N issues than when the same N issuse are handled separately.
    • Mean gain gives the governor's average gain from having an item veto (similar to the "item veto's average utility effect" column), but ONLY in those situations where the governor actually benefitted from having an item veto. (As "freq" rises, this column comes to resemble the corresponding column in "utility gains from bundling.")

Adjust simulation parameters

Those simulations were run using the parameters shown below. You may adjust all the simulation's parameters except for the number of trials. All these numbers represent only "utils"; they are on an arbitrary scale. The only thing that really matters is how each number compares to the standard deviation of the normal distribution from which we draw ideal points. The randomization function is seeded; that means you will always obtain the same results from the same inputs below.

Integers (round numbers) only, or the simulation may ignore your inputs.